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You have a Fabric workspace. You have semi-structured data. You need to read the data by using T-SQL, KQL, and Apache Spark. The data will only be written by using Spark. What should you use to store the data?. a lakehouse. an eventhouse. a datamart. a warehouse.

You have a Fabric workspace that contains a warehouse named Warehouse1. You have an on-premises Microsoft SQL Server database named Database1 that is accessed by using an onpremises data gateway. You need to copy data from Database1 to Warehouse1. Which item should you use?. a Dataflow Gen1 dataflow. a data pipeline. a KQL queryset. a notebook.

You have a Fabric workspace that contains a warehouse named Warehouse1. You have an on-premises Microsoft SQL Server database named Database1 that is accessed by using an onpremises data gateway. You need to copy data from Database1 to Warehouse1. Which item should you use? A.an Apache Spark job definition B.a data pipeline C.a Dataflow Gen1 dataflow D.an eventstream. an Apache Spark job definition. a data pipeline. a Dataflow Gen1 dataflow. an eventstream.

You have a Fabric F32 capacity that contains a workspace. The workspace contains a warehouse named DW1 that is modelled by using MD5 hash surrogate keys. DW1 contains a single fact table that has grown from 200 million rows to 500 million rows during the past year. You have Microsoft Power BI reports that are based on Direct Lake. The reports show year-over-year values. Users report that the performance of some of the reports has degraded over time and some visuals show errors. You need to resolve the performance issues. The solution must meet the following requirements: Provide the best query performance. Minimize operational costs. Which should you do?. Change the MD5 hash to SHA256. Increase the capacity. Enable V-Order. Modify the surrogate keys to use a different data type. Create views.

You have a Fabric workspace that contains a lakehouse named Lakehouse1. Data is ingested into Lakehouse1 as one flat table. The table contains the following columns. You plan to load the data into a dimensional model and implement a star schema. From the original flat table, you create two tables named FactSales and DimProduct. You will track changes in DimProduct. You need to prepare the data. Which three columns should you include in the DimProduct table? Each correct answer presents part of the solution. NOTE: Each correct selection is worth one point. Date. ProductName. ProductColor. TransactionID. SalesAmount. ProductID.

You have a Fabric workspace named Workspace1 that contains a notebook named Notebook1. In Workspace1, you create a new notebook named Notebook2. You need to ensure that you can attach Notebook2 to the same Apache Spark session as Notebook1. What should you do?. Enable high concurrency for notebooks. Enable dynamic allocation for the Spark pool. Change the runtime version. Increase the number of executors.

You have a Fabric workspace named Workspace1 that contains a lakehouse named Lakehouse1. Lakehouse1 contains the following tables: Orders - Customer - Employee - The Employee table contains Personally Identifiable Information (PII). A data engineer is building a workflow that requires writing data to the Customer table, however, the user does NOT have the elevated permissions required to view the contents of the Employee table. You need to ensure that the data engineer can write data to the Customer table without reading data from the Employee table. Which three actions should you perform? Each correct answer presents part of the solution. NOTE: Each correct selection is worth one point. Share Lakehouse1 with the data engineer. Assign the data engineer the Contributor role for Workspace2. Assign the data engineer the Viewer role for Workspace2. Assign the data engineer the Contributor role for Workspace1. Migrate the Employee table from Lakehouse1 to Lakehouse2. Create a new workspace named Workspace2 that contains a new lakehouse named Lakehouse2. Assign the data engineer the Viewer role for Workspace1.

You have a Fabric warehouse named DW1. DW1 contains a table that stores sales data and is used by multiple sales representatives. You plan to implement row-level security (RLS). You need to ensure that the sales representatives can see only their respective data. Which warehouse object do you require to implement RLS?. STORED PROCEDURE. CONSTRAINT. SCHEMA. FUNCTION.

You have a Fabric workspace named Workspace1_DEV that contains the following items: 10 reports Four notebooks - Three lakehouses - Two data pipelines - Two Dataflow Gen1 dataflows - Three Dataflow Gen2 dataflows - Five semantic models that each has a scheduled refresh policy You create a deployment pipeline named Pipeline1 to move items from Workspace1_DEV to a new workspace named Workspace1_TEST. You deploy all the items from Workspace1_DEV to Workspace1_TEST. For each of the following statements, select Yes if the statement is true. Otherwise, select No. NOTE: Each correct selection is worth one point. Answer: No/Yes/No.

You have a Fabric deployment pipeline that uses three workspaces named Dev, Test, and Prod. You need to deploy an eventhouse as part of the deployment process. What should you use to add the eventhouse to the deployment process?. GitHub Actions. a deployment pipeline. an Azure DevOps pipeline.

You have a Fabric workspace named Workspace1 that contains a warehouse named Warehouse1. You plan to deploy Warehouse1 to a new workspace named Workspace2. As part of the deployment process, you need to verify whether Warehouse1 contains invalid references. The solution must minimize development effort. What should you use?. a database project. a deployment pipeline. a Python script. a T-SQL script.

You have a Fabric workspace that contains a Real-Time Intelligence solution and an eventhouse. Users report that from OneLake file explorer, they cannot see the data from the eventhouse. You enable OneLake availability for the eventhouse. What will be copied to OneLake?. only data added to new databases that are added to the eventhouse. only the existing data in the eventhouse. no data. both new data and existing data in the eventhouse. only new data added to the eventhouse.

You have a Fabric workspace named Workspace1. You plan to integrate Workspace1 with Azure DevOps. You will use a Fabric deployment pipeline named deployPipeline1 to deploy items from Workspace1 to higher environment workspaces as part of a medallion architecture. You will run deployPipeline1 by using an API call from an Azure DevOps pipeline. You need to configure API authentication between Azure DevOps and Fabric. Which type of authentication should you use?. service principal. Microsoft Entra username and password. managed private endpoint. workspace identity.

Stores only. Products only. Stores and Products only. Products, Stores, and Trips. Trips only. Products and Trips only.

You have a Fabric workspace named Workspace1 that contains an Apache Spark job definition named Job1. You have an Azure SQL database named Source1 that has public internet access disabled. You need to ensure that Job1 can access the data in Source1. What should you create?. an on-premises data gateway. a managed private endpoint. an integration runtime. a data management gateway.

You have an Azure Data Lake Storage Gen2 account named storage1 and an Amazon S3 bucket named storage2. You have the Delta Parquet files shown in the following table. You have a Fabric workspace named Workspace1 that has the cache for shortcuts enabled. Workspace1 contains a lakehouse named Lakehouse1. Lakehouse1 has the following shortcuts: A shortcut to ProductFile aliased as Products A shortcut to StoreFile aliased as Stores A shortcut to TripsFile aliased as Trips The data from which shortcuts will be retrieved from the cache?. Trips and Stores only. Products and Store only. Stores only. Products only. Products, Stores, and Trips.

You have a Fabric workspace that contains a warehouse named DW1. DW1 contains the following tables and columns. You need to create an output that presents the summarized values of all the order quantities by year and product. The results must include a summary of the order quantities at the year level for all the products. How should you complete the code? To answer, select the appropriate options in the answer area. NOTE: Each correct selection is worth one point. 1. SELECT CAST 2. SELECT CONVERT 3. SELECT YEAR: CORRECT (SO.ModifiedDate) AS OrderDate ,P.Name AS ProductName ,SUM(SO.OrderQty) AS OrderQty FROM [dbo]. [SalesOrderDetail] so INNER JOIN [dbo]. [Product] P ON P.ProductID = SO.ProductID GROUP BY 1. CUBE(YEAR(SO.ModifiedDate), P.Name) 2. GROUPING SETS ((YEAR(SO.ModifiedDate), P.Name), (YEAR(SO.ModifiedDate))) 3. ROLLUP(YEAR(SO.ModifiedDate), P.Name): CORRECT YEAR(SO.ModifiedDate), P.Name ORDER BY OrderDate CORRECT: SELECT YEAR(SO.ModifiedDate) AS OrderDate, P.Name AS ProductName, SUM(SO.OrderQty) AS OrderQty FROM [dbo].[SalesOrderDetail] SO INNER JOIN [dbo].[Product] P ON P.ProductID = SO.ProductID GROUP BY ROLLUP(YEAR(SO.ModifiedDate), P.Name) ORDER BY OrderDate.

You have a Fabric workspace named Workspace1 that contains the items shown in the following table. For Model1, the Keep your Direct Lake data up to date option is disabled. You need to configure the execution of the items to meet the following requirements: Notebook1 must execute every weekday at 8:00 AM. Notebook2 must execute when a file is saved to an Azure Blob Storage container. Model1 must refresh when Notebook1 has executed successfully. How should you orchestrate each item? To answer, select the appropriate options in the answer area. NOTE: Each correct selection is worth one point.

Your company has a sales department that uses two Fabric workspaces named Workspace1 and Workspace2. The company decides to implement a domain strategy to organize the workspaces. You need to ensure that a user can perform the following tasks: Create a new domain for the sales department. Create two subdomains: one for the east region and one for the west region. Assign Workspace1 to the east region subdomain. Assign Workspace2 to the west region subdomain. The solution must follow the principle of least privilege. Which role should you assign to the user?. workspace Admin. domain admin. domain contributor. Fabric admin.

You have a Fabric workspace named Workspace1 that contains a warehouse named DW1 and a data pipeline named Pipeline1. You plan to add a user named User3 to Workspace1. You need to ensure that User3 can perform the following actions: View all the items in Workspace1. Update the tables in DW1. The solution must follow the principle of least privilege. You already assigned the appropriate object-level permissions to DW1. Which workspace role should you assign to User3?. Admin. Member. Viewer. Contributor.

You have a Fabric capacity that contains a workspace named Workspace1. Workspace1 contains a lakehouse named Lakehouse1, a data pipeline, a notebook, and several Microsoft Power BI reports. A user named User1 wants to use SQL to analyze the data in Lakehouse1. You need to configure access for User1. The solution must meet the following requirements: Provide User1 with read access to the table data in Lakehouse1. Prevent User1 from using Apache Spark to query the underlying files in Lakehouse1. Prevent User1 from accessing other items in Workspace1. What should you do?. Share Lakehouse1 with User1 directly and select Read all SQL endpoint data. Assign User1 the Viewer role for Workspace1. Share Lakehouse1 with User1 and select Read all SQL endpoint data. Share Lakehouse1 with User1 directly and select Build reports on the default semantic model. Assign User1 the Member role for Workspace1. Share Lakehouse1 with User1 and select Read all SQL endpoint data.

DRAG DROP - You are implementing the following data entities in a Fabric environment: Entity1: Available in a lakehouse and contains data that will be used as a core organization entity Entity2: Available in a semantic model and contains data that meets organizational standards Entity3: Available in a Microsoft Power BI report and contains data that is ready for sharing and reuse Entity4: Available in a Power BI dashboard and contains approved data for executive-level decision making Your company requires that specific governance processes be implemented for the data. You need to apply endorsement badges to the entities based on each entity’s use case. Which badge should you apply to each entity? To answer, drag the appropriate badges the correct entities. Each badge may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content. NOTE: Each correct selection is worth one point.

HOTSPOT - You have three users named User1, User2, and User3. You have the Fabric workspaces shown in the following table. +--------------+-----------------+ | Name | Workspace admin | +--------------+-----------------+ | Workspace1 | User1 | | Workspace2 | User2 | +--------------+-----------------+ You have a security group named Group1 that contains User1 and User3. The Fabric admin creates the domains shown in the following table. +--------------+-----------------+ | Name | Domain admin | +--------------+-----------------+ | Domain1 | User1 | | Domain2 | User2 | +--------------+-----------------+ User1 creates a new workspace named Workspace3. You add Group1 to the default domain of Domain1. For each of the following statements, select Yes if the statement is true. Otherwise, select No. NOTE: Each correct selection is worth one point.

You have two Fabric workspaces named Workspace1 and Workspace2. You have a Fabric deployment pipeline named deployPipeline1 that deploys items from Workspace1 to Workspace2. DeployPipeline1 contains all the items in Workspace1. You recently modified the items in Workspaces1. The workspaces currently contain the items shown in the following table. Items in Workspace1 that have the same name as items in Workspace2 are currently paired. You need to ensure that the items in Workspace1 overwrite the corresponding items in Workspace2. The solution must minimize effort. What should you do?. Delete all the items in Workspace2, and then run deployPipeline1. Rename each item in Workspace2 to have the same name as the items in Workspace1. Back up the items in Workspace2, and then run deployPipeline1. Run deployPipeline1 without modifying the items in Workspace2.

You have a Fabric workspace named Workspace1 that contains a data pipeline named Pipeline1 and a lakehouse named Lakehouse1. You have a deployment pipeline named deployPipeline1 that deploys Workspace1 to Workspace2. You restructure Workspace1 by adding a folder named Folder1 and moving Pipeline1 to Folder1. You use deployPipeline1 to deploy Workspace1 to Workspace2. What occurs to Workspace2?. Folder1 is created, Pipeline1 moves to Folder1, and Lakehouse1 is deployed. Only Pipeline1 and Lakehouse1 are deployed. Folder1 is created, and Pipeline1 and Lakehouse1 move to Folder1. Only Folder1 is created and Pipeline1 moves to Folder1.

DRAG DROP - Your company has a team of developers. The team creates Python libraries of reusable code that is used to transform data. You create a Fabric workspace name Workspace1 that will be used to develop extract, transform, and load (ETL) solutions by using notebooks. You need to ensure that the libraries are available by default to new notebooks in Workspace1. Which three actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.

You have a Fabric workspace that contains a lakehouse and a notebook named Notebook1. Notebook1 reads data into a DataFrame from a table named Table1 and applies transformation logic. The data from the DataFrame is then written to a new Delta table named Table2 by using a merge operation. You need to consolidate the underlying Parquet files in Table1. Which command should you run?. VACUUM. BROADCAST. OPTIMIZE. CACHE.

You have five Fabric workspaces. You are monitoring the execution of items by using Monitoring hub. You need to identify in which workspace a specific item runs. Which column should you view in Monitoring hub?. Start time. Capacity. Activity name. Submitter. Item type. Job type. Location.

You have a Fabric workspace that contains a warehouse named DW1. DW1 is loaded by using a notebook named Notebook1. You need to identify which version of Delta was used when Notebook1 was executed. What should you use?. Real-Time hub. OneLake data hub. the Admin monitoring workspace. Fabric Monitor. the Microsoft Fabric Capacity Metrics app.

DRAG DROP - You have a Fabric workspace that contains a warehouse named Warehouse1. In Warehouse1, you create a table named DimCustomer by running the following statement. CREATE TABLE dbo.DimCustomer ( CustomerKey VARCHAR(255) NOT NULL, Name VARCHAR(255) NOT NULL, Email VARCHAR(255) NOT NULL ); You need to set the Customerkey column as a primary key of the DimCustomer table. Which three code segments should you run in sequence? To answer, move the appropriate code segments from the list of code segments to the answer area and arrange them in the correct order.

You have a Fabric workspace that contains a semantic model named Model1. You need to dynamically execute and monitor the refresh progress of Model1. What should you use?. dynamic management views in Microsoft SQL Server Management Studio (SSMS). Monitoring hub. dynamic management views in Azure Data Studio. a semantic link in a notebook.

Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution. After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen. You have a Fabric eventstream that loads data into a table named Bike_Location in a KQL database. The table contains the following columns: BikepointID - Street - Neighbourhood - No_Bikes - No_Empty_Docks - Timestamp - You need to apply transformation and filter logic to prepare the data for consumption. The solution must return data for a neighbourhood named Sands End when No_Bikes is at least 15. The results must be ordered by No_Bikes in ascending order. Solution: You use the following code segment: bike_location | filter Neighbourhood == "Sands End" and No_Bikes >= 15 | sort by No_Bikes | project BikepointID, Street, Neighbourhood, No_Bikes, No_Empty_Docks, Timestamp | project BikepointID, Street, Neighbourhood, No_Bikes, No_Empty_Docks, Timestamp Does this meet the goal?. Yes. No.

Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution. After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen. You have a Fabric eventstream that loads data into a table named Bike_Location in a KQL database. The table contains the following columns: BikepointID - Street - Neighbourhood - No_Bikes - No_Empty_Docks - Timestamp - You need to apply transformation and filter logic to prepare the data for consumption. The solution must return data for a neighbourhood named Sands End when No_Bikes is at least 15. The results must be ordered by No_Bikes in ascending order. Solution: You use the following code segment: bike_location | filter Neighbourhood == "Sands End" and No_Bikes >= 15 | order by No_Bikes | project BikepointID, Street, Neighbourhood, No_Bikes, No_Empty_Docks, Timestamp. Yes. No.

Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution. After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen. You have a Fabric eventstream that loads data into a table named Bike_Location in a KQL database. The table contains the following columns: BikepointID - Street - Neighbourhood - No_Bikes - No_Empty_Docks - Timestamp - You need to apply transformation and filter logic to prepare the data for consumption. The solution must return data for a neighbourhood named Sands End when No_Bikes is at least 15. The results must be ordered by No_Bikes in ascending order. Solution: You use the following code segment: bike_location | filter Neighbourhood == "Sands End" and No_Bikes >= 15 | sort by No_Bikes asc | project BikepointID, Street, Neighbourhood, No_Bikes, No_Empty_Docks, Timestamp Does this meet the goal?. Yes. No.

Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution. After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen. You have a Fabric eventstream that loads data into a table named Bike_Location in a KQL database. The table contains the following columns: BikepointID - Street - Neighbourhood - No_Bikes - No_Empty_Docks - Timestamp - You need to apply transformation and filter logic to prepare the data for consumption. The solution must return Question: 36 Exam Heist data for a neighbourhood named Sands End when No_Bikes is at least 15. The results must be ordered by No_Bikes in ascending order. Solution: You use the following code segment: SELECT BikepointID, Street, Neighbourhood, No_Bikes, No_Empty_Docks, Timestamp FROM bike_location WHERE neighbourhood = 'Sands End' AND no_bikes >= 15 ORDER BY no_bikes Does this meet the goal?. Yes. No.

Case Study - This is a case study. Case studies are not timed separately. You can use as much exam time as you would like to complete each case. However, there may be additional case studies and sections on this exam. You must manage your time to ensure that you are able to complete all questions included on this exam in the time provided. To answer the questions included in a case study, you will need to reference information that is provided in the case study. Case studies might contain exhibits and other resources that provide more information about the scenario that is described in the case study. Each question is independent of the other questions in this case study. At the end of this case study, a review screen will appear. This screen allows you to review your answers and to make changes before you move to the next section of the exam. After you begin a new section, you cannot return to this section. To start the case study - To display the first question in this case study, click the Next button. Use the buttons in the left pane to explore the content of the case study before you answer the questions. Clicking these buttons displays information such as business requirements, existing environment, and problem statements. If the case study has an All Information tab, note that the information displayed is identical to the information displayed on the subsequent tabs. When you are ready to answer a question, click the Question button to return to the question. Overview - Litware, Inc. is a publishing company that has an online bookstore and several retail bookstores worldwide. Litware also manages an online advertising business for the authors it represents. Existing Environment. Fabric Environment Litware has a Fabric workspace named Workspace1. High concurrency is enabled for Workspace1. The company has a data engineering team that uses Python for data processing. Existing Environment. Data Processing The retail bookstores send sales data at the end of each business day, while the online bookstore constantly provides logs and sales data to a central enterprise resource planning (ERP) system. Litware implements a medallion architecture by using the following three layers: bronze, silver, and gold. The sales data is ingested from the ERP system as Parquet files that land in the Files folder in a lakehouse. Notebooks are used to transform the files in a Delta table for the bronze and silver layers. The gold layer is in a warehouse that has V-Order disabled. Litware has image files of book covers in Azure Blob Storage. The files are loaded into the Files folder. Existing Environment. Sales Data Month-end sales data is processed on the first calendar day of each month. Data that is older than one month never changes. In the source system, the sales data refreshes every six hours starting at midnight each day. The sales data is captured in a Dataflow Gen1 dataflow. When the dataflow runs, new and historical data is captured. The dataflow captures the following fields of the source: •Sales Date •Author •Price •Units •SKU A table named AuthorSales stores the sales data that relates to each author. The table contains a column named AuthorEmail. Authors authenticate to a guest Fabric tenant by using their email address. Existing Environment. Security Groups Litware has the following security groups: •Sales •Fabric Admins •Streaming Admins Existing Environment. Performance Issues Business users perform ad-hoc queries against the warehouse. The business users indicate that reports against the warehouse sometimes run for two hours and fail to load as expected. Upon further investigation, the data engineering team receives the following error message when the reports fail to load: “The SQL query failed while running.” The data engineering team wants to debug the issue and find queries that cause more than one failure. When the authors have new book releases, there is often an increase in sales activity. This increase slows the data ingestion process. The company’s sales team reports that during the last month, the sales data has NOT been up-to-date when they arrive at work in the morning. Requirements. Planned Changes - Litware recently signed a contract to receive book reviews. The provider of the reviews exposes the data in Amazon Simple Storage Service (Amazon S3) buckets. Litware plans to manage Search Engine Optimization (SEO) for the authors. The SEO data will be streamed from a REST API. Requirements. Version Control - Litware plans to implement a version control solution in Fabric that will use GitHub integration and follow the principle of least privilege. Requirements. Governance Requirements To control data platform costs, the data platform must use only Fabric services and items. Additional Azure resources must NOT be provisioned. Requirements. Data Requirements - Litware identifies the following data requirements: •Process the SEO data in near-real-time (NRT). •Make the book reviews available in the lakehouse without making a copy of the data. •When a new book cover image arrives in the Files folder, process the image as soon as possible. You need to ensure that processes for the bronze and silver layers run in isolation. How should you configure the Apache Spark settings?. Disable high concurrency. Create a custom pool. Modify the number of executors. Set the default environment.

Case Study - This is a case study. Case studies are not timed separately. You can use as much exam time as you would like to complete each case. However, there may be additional case studies and sections on this exam. You must manage your time to ensure that you are able to complete all questions included on this exam in the time provided. To answer the questions included in a case study, you will need to reference information that is provided in the case study. Case studies might contain exhibits and other resources that provide more information about the scenario that is described in the case study. Each question is independent of the other questions in this case study. At the end of this case study, a review screen will appear. This screen allows you to review your answers and to make changes before you move to the next section of the exam. After you begin a new section, you cannot return to this section. To start the case study - To display the first question in this case study, click the Next button. Use the buttons in the left pane to explore the content of the case study before you answer the questions. Clicking these buttons displays information such as business requirements, existing environment, and problem statements. If the case study has an All Information tab, note that the information displayed is identical to the information displayed on the subsequent tabs. When you are ready to answer a question, click the Question button to return to the question. Overview - Litware, Inc. is a publishing company that has an online bookstore and several retail bookstores worldwide. Litware also manages an online advertising business for the authors it represents. Existing Environment. Fabric Environment Litware has a Fabric workspace named Workspace1. High concurrency is enabled for Workspace1. The company has a data engineering team that uses Python for data processing. Existing Environment. Data Processing The retail bookstores send sales data at the end of each business day, while the online bookstore constantly provides logs and sales data to a central enterprise resource planning (ERP) system. Litware implements a medallion architecture by using the following three layers: bronze, silver, and gold. The sales data is ingested from the ERP system as Parquet files that land in the Files folder in a lakehouse. Notebooks are used to transform the files in a Delta table for the bronze and silver layers. The gold layer is in a warehouse that has V-Order disabled. Litware has image files of book covers in Azure Blob Storage. The files are loaded into the Files folder. Existing Environment. Sales Data Month-end sales data is processed on the first calendar day of each month. Data that is older than one month never changes. In the source system, the sales data refreshes every six hours starting at midnight each day. The sales data is captured in a Dataflow Gen1 dataflow. When the dataflow runs, new and historical data is captured. The dataflow captures the following fields of the source: •Sales Date •Author •Price •Units •SKU A table named AuthorSales stores the sales data that relates to each author. The table contains a column named AuthorEmail. Authors authenticate to a guest Fabric tenant by using their email address. Existing Environment. Security Groups Litware has the following security groups: •Sales •Fabric Admins •Streaming Admins Existing Environment. Performance Issues Business users perform ad-hoc queries against the warehouse. The business users indicate that reports against the warehouse sometimes run for two hours and fail to load as expected. Upon further investigation, the data engineering team receives the following error message when the reports fail to load: “The SQL query failed while running.” The data engineering team wants to debug the issue and find queries that cause more than one failure. When the authors have new book releases, there is often an increase in sales activity. This increase slows the data ingestion process. The company’s sales team reports that during the last month, the sales data has NOT been up-to-date when they arrive at work in the morning. Requirements. Planned Changes - Litware recently signed a contract to receive book reviews. The provider of the reviews exposes the data in Amazon Simple Storage Service (Amazon S3) buckets. Litware plans to manage Search Engine Optimization (SEO) for the authors. The SEO data will be streamed from a REST API. Requirements. Version Control - Litware plans to implement a version control solution in Fabric that will use GitHub integration and follow the principle of least privilege. Requirements. Governance Requirements To control data platform costs, the data platform must use only Fabric services and items. Additional Azure resources must NOT be provisioned. Requirements. Data Requirements Litware identifies the following data requirements: •Process the SEO data in near-real-time (NRT). •Make the book reviews available in the lakehouse without making a copy of the data. •When a new book cover image arrives in the Files folder, process the image as soon as possible. You need to ensure that the authors can see only their respective sales data. How should you complete the statement? To answer, drag the appropriate values the correct targets. Each value may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content. NOTE: Each correct selection is worth one point.

Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution. After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen. You have an Azure key vault named KeyVault1 that contains secrets. You have a Fabric workspace named Workspace1. Workspace contains a notebook named Notebook1 that performs the following tasks: •Loads stage data to the target tables in a lakehouse •Triggers the refresh of a semantic model You plan to add functionality to Notebook1 that will use the Fabric API to monitor the semantic model refreshes. You need to retrieve the registered application ID and secret from KeyVault1 to generate the authentication token. Solution: You use the following code segment: Use notebookutils.credentials.getSecret and specify the key vault URL and key vault secret. Does this meet the goal?. Yes. No.

Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution. After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen. You have an Azure key vault named KeyVault1 that contains secrets. You have a Fabric workspace named Workspace1. Workspace contains a notebook named Notebook1 that performs the following tasks: •Loads stage data to the target tables in a lakehouse •Triggers the refresh of a semantic model You plan to add functionality to Notebook1 that will use the Fabric API to monitor the semantic model refreshes. You need to retrieve the registered application ID and secret from KeyVault1 to generate the authentication token. Solution: You use the following code segment: Use notebookutils.credentials.putSecret and specify the key vault URL and key vault secret. Does this meet the goal?. Yes. No.

Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution. After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen. You have an Azure key vault named KeyVault1 that contains secrets. You have a Fabric workspace named Workspace1. Workspace contains a notebook named Notebook1 that performs the following tasks: •Loads stage data to the target tables in a lakehouse •Triggers the refresh of a semantic model You plan to add functionality to Notebook1 that will use the Fabric API to monitor the semantic model refreshes. You need to retrieve the registered application ID and secret from KeyVault1 to generate the authentication token. Solution: You use the following code segment: Use notebookutils.credentials.getSecret and specify the key vault URL and the name of a linked service. Does this meet the goal?. Yes. No.

You have two Fabric notebooks named Load_Salesperson and Load_Orders that read data from Parquet files in a lakehouse. Load_Salesperson writes to a Delta table named dim_salesperson. Load_Orders writes to a Delta table named fact_orders and is dependent on the successful execution of Load_Salesperson. You need to implement a pattern to dynamically execute Load_Salesperson and Load_Orders in the appropriate order by using a notebook. How should you complete the code? To answer, drag the appropriate values the correct targets. Each value may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content. NOTE: Each correct selection is worth one point.

You have a Fabric workspace named Workspace1 that contains a warehouse named Warehouse2. A team of data analysts has Viewer role access to Workspace1. You create a table by running the following statement. CREATE TABLE [warehouse2].[dbo].[CreditCard] ( CreditCard varchar(20) NOT NULL ,CreditCardType varchar(10) NOT NULL) GO You need to ensure that the team can view only the first two characters and the last four characters of the CreditCard attribute. How should you complete the statement? To answer, select the appropriate options in the answer area. NOTE: Each correct selection is worth one point.

You are building a data orchestration pattern by using a Fabric data pipeline named Dynamic Data Copy as shown in the exhibit. (Click the Exhibit tab.).

You have a Fabric workspace that contains a warehouse named Warehouse1. Warehouse1 contains a table named DimCustomers. DimCustomers contains the following columns: •CustomerName •CustomerID •BirthDate •EmailAddress You need to configure security to meet the following requirements: •BirthDate in DimCustomer must be masked and display 1900-01-01. •EmailAddress in DimCustomer must be masked and display only the first leading character and the last five characters. How should you complete the statement? To answer, select the appropriate options in the answer area. NOTE: Each correct selection is worth one point.

You have a Fabric workspace named Workspace1 that contains the following items: •A Microsoft Power BI report named Report1 •A Power BI dashboard named Dashboard1 •A semantic model named Model1 •A lakehouse name Lakehouse1 Your company requires that specific governance processes be implemented for the items. Which items can you endorse in Fabric?. Lakehouse1, Model1, and Dashboard1 only. Lakehouse1, Model1, Report1 and Dashboard1. Report1 and Dashboard1 only. Model1, Report1, and Dashboard1 only. Lakehouse1, Model1, and Report1 only.

You have a Fabric workspace named Workspace1. Your company acquires GitHub licenses. You need to configure source control for Workpace1 to use GitHub. The solution must follow the principle of least privilege. Which permissions do you require to ensure that you can commit code to GitHub?. Actions (Read and write) and Contents (Read and write). Actions (Read and write) only. Contents (Read and write) only. Contents (Read) and Commit statuses (Read and write).

You have a Fabric workspace named Workspace1. You plan to configure Git integration for Workspace1 by using an Azure DevOps Git repository. An Azure DevOps admin creates the required artifacts to support the integration of Workspace1. Which details do you require to perform the integration?. the organization, project, Git repository, and branch. the personal access token (PAT) for Git authentication and the Git repository URL. the project, Git repository, branch, and Git folder. the Git repository URL and the Git folder.

You have a Fabric workspace that contains a lakehouse and a semantic model named Model1. You use a notebook named Notebook1 to ingest and transform data from an external data source. You need to execute Notebook1 as part of a data pipeline named Pipeline1. The process must meet the following requirements: •Run daily at 07:00 AM UTC. •Attempt to retry Notebook1 twice if the notebook fails. •After Notebook1 executes successfully, refresh Model1. Which three actions should you perform? Each correct answer presents part of the solution. NOTE: Each correct selection is worth one point. Place the Semantic model refresh activity after the Notebook activity and link the activities by using the On success condition. From the Schedule settings of Pipeline1, set the time zone to UTC. Set the Retry setting of the Notebook activity to 2. From the Schedule settings of Notebook1, set the time zone to UTC. Set the Retry setting of the Semantic model refresh activity to 2. Place the Semantic model refresh activity after the Notebook activity and link the activities by using an On completion condition.

You have a Fabric workspace that contains a lakehouse named Lakehouse1. You plan to create a data pipeline named Pipeline1 to ingest data into Lakehouse1. You will use a parameter named param1 to pass an external value into Pipeline1. The param1 parameter has a data type of int. You need to ensure that the pipeline expression returns param1 as an int value. How should you specify the parameter value?. "@pipeline().parameters.param1". "@ pipeline().parameters.param1". "@ pipeline().parameters.[param1]". "@@ pipeline().parameters.param1".

You have a Fabric workspace named Workspace1 that contains a lakehouse named Lakehouse1. Workspace1 contains the following items: •A Dataflow Gen2 dataflow that copies data from an on-premises Microsoft SQL Server database to Lakehouse1 •A notebook that transforms files and loads the data to Lakehouse1 •A data pipeline that loads a CSV file to Lakehouse1 You need to develop an orchestration solution in Fabric that will load each item one after the other. The solution must be scheduled to run every 15 minutes. Which type of item should you use?. notebook. warehouse. Dataflow Gen2 dataflow. data pipeline.

You are building a Fabric notebook named MasterNotebook1 in a workspace. MasterNotebook1 contains the following code. DAG = { "activities": [ { "name": "execute_notebook_1", "path": "notebook_01", "timeoutPerCellInseconds": 600, "args"{ "input_value": "999" }, "retry": 1, "retryIntervalInSeconds": 30 }, { "name": "execute_notebook_2", "path": "notebook_02", "timeoutPerCellInSeconds": 400, "args": { "input_value": "888" }, "retry": 1, "retryIntervalInSeconds": 30 }, { "name": "execute_notebook_3", "path": "notebook_03", "timeoutPerCellInSeconds": 600, "args": { "input_value": "777" }, "retry": 1, "retryIntervalInSeconds": 30 } ], "timeoutInSeconds": 43200, "concurrency": 0 } mssparkutils.notebook.runMultiple(DAG,{"displayDAGViaGraphviz": True}) You need to ensure that the notebooks are executed in the following sequence: 1. Notebook_03 2. Notebook_01 3. Notebook_02 Which two actions should you perform? Each correct answer presents part of the solution. NOTE: Each correct selection is worth one point. Move the declaration of Notebook_02 to the bottom of the Directed Acyclic Graph (DAG) definition. Add dependencies to the execution of Notebook_03. Split the Directed Acyclic Graph (DAG) definition into three separate definitions. Add dependencies to the execution of Notebook_02. Change the concurrency to 3. Move the declaration of Notebook_03 to the top of the Directed Acyclic Graph (DAG) definition.

You have a Fabric workspace that contains a data pipeline named Pipeline1 as shown in the exhibit. (Click the Exhibit tab.). Copy_kdi will run first, and then Execute procedure1 will run. Execute procedure1 will run first, and then Copy_kdi will run. Execute procedure1 will run and Copy_kdi will be skipped. Copy_kdi will run and Execute procedure1 will be skipped. Both activities will run simultaneously. Both activities will be skipped.

Case Study - This is a case study. Case studies are not timed separately. You can use as much exam time as you would like to complete each case. However, there may be additional case studies and sections on this exam. You must manage your time to ensure that you are able to complete all questions included on this exam in the time provided. To answer the questions included in a case study, you will need to reference information that is provided in the case study. Case studies might contain exhibits and other resources that provide more information about the scenario that is described in the case study. Each question is independent of the other questions in this case study. At the end of this case study, a review screen will appear. This screen allows you to review your answers and to make changes before you move to the next section of the exam. After you begin a new section, you cannot return to this section. To start the case study - To display the first question in this case study, click the Next button. Use the buttons in the left pane to explore the content of the case study before you answer the questions. Clicking these buttons displays information such as business requirements, existing environment, and problem statements. If the case study has an All Information tab, note that the information displayed is identical to the information displayed on the subsequent tabs. When you are ready to answer a question, click the Question button to return to the question. Overview. Company Overview - Contoso, Ltd. is an online retail company that wants to modernize its analytics platform by moving to Fabric. The company plans to begin using Fabric for marketing analytics. Overview. IT Structure - The company’s IT department has a team of data analysts and a team of data engineers that use analytics systems. The data engineers perform the ingestion, transformation, and loading of data. They prefer to use Python or SQL to transform the data. The data analysts query data and create semantic models and reports. They are qualified to write queries in Power Query and T-SQL. Existing Environment. Fabric - Contoso has an F64 capacity named Cap1. All Fabric users are allowed to create items. Contoso has two workspaces named WorkspaceA and WorkspaceB that currently use Pro license mode. Existing Environment. Source Systems Contoso has a point of sale (POS) system named POS1 that uses an instance of SQL Server on Azure Virtual Machines in the same Microsoft Entra tenant as Fabric. The host virtual machine is on a private virtual network that has public access blocked. POS1 contains all the sales transactions that were processed on the company’s website. The company has a software as a service (SaaS) online marketing app named MAR1. MAR1 has seven entities. The entities contain data that relates to email open rates and interaction rates, as well as website interactions. The data can be exported from MAR1 by calling REST APIs. Each entity has a different endpoint. Contoso has been using MAR1 for one year. Data from prior years is stored in Parquet files in an Amazon Simple Storage Service (Amazon S3) bucket. There are 12 files that range in size from 300 MB to 900 MB and relate to email interactions. Existing Environment. Product Data POS1 contains a product list and related data. The data comes from the following three tables: •Products •ProductCategories •ProductSubcategories In the data, products are related to product subcategories, and subcategories are related to product categories. Existing Environment. Azure - Contoso has a Microsoft Entra tenant that has the following mail-enabled security groups: •DataAnalysts: Contains the data analysts •DataEngineers: Contains the data engineers Contoso has an Azure subscription. The company has an existing Azure DevOps organization and creates a new project for repositories that relate to Fabric. Existing Environment. User Problems The VP of marketing at Contoso requires analysis on the effectiveness of different types of email content. It typically takes a week to manually compile and analyze the data. Contoso wants to reduce the time to less than one day by using Fabric. The data engineering team has successfully exported data from MAR1. The team experiences transient connectivity errors, which causes the data exports to fail. Requirements. Planned Changes - Contoso plans to create the following two lakehouses: •Lakehouse1: Will store both raw and cleansed data from the sources •Lakehouse2: Will serve data in a dimensional model to users for analytical queries Additional items will be added to facilitate data ingestion and transformation. Contoso plans to use Azure Repos for source control in Fabric. Requirements. Technical Requirements The new lakehouses must follow a medallion architecture by using the following three layers: bronze, silver, and gold. There will be extensive data cleansing required to populate the MAR1 data in the silver layer, including deduplication, the handling of missing values, and the standardizing of capitalization. Each layer must be fully populated before moving on to the next layer. If any step in populating the lakehouses fails, an email must be sent to the data engineers. Data imports must run simultaneously, when possible. The use of email data from the Amazon S3 bucket must meet the following requirements: •Minimize egress costs associated with cross-cloud data access. •Prevent saving a copy of the raw data in the lakehouses. Items that relate to data ingestion must meet the following requirements: •The items must be source controlled alongside other workspace items. •Ingested data must land in the bronze layer of Lakehouse1 in the Delta format. •No changes other than changes to the file formats must be implemented before the data lands in the bronze layer. •Development effort must be minimized and a built-in connection must be used to import the source data. •In the event of a connectivity error, the ingestion processes must attempt the connection again. Lakehouses, data pipelines, and notebooks must be stored in WorkspaceA. Semantic models, reports, and dataflows must be stored in WorkspaceB. Once a week, old files that are no longer referenced by a Delta table log must be removed. Requirements. Data Transformation In the POS1 product data, ProductID values are unique. The product dimension in the gold layer must include only active products from product list. Active products are identified by an IsActive value of 1. Some product categories and subcategories are NOT assigned to any product. They are NOT analytically relevant and must be omitted from the product dimension in the gold layer. Requirements. Data Security - Security in Fabric must meet the following requirements: •The data engineers must have read and write access to all the lakehouses, including the underlying files. •The data analysts must only have read access to the Delta tables in the gold layer. •The data analysts must NOT have access to the data in the bronze and silver layers. •The data engineers must be able to commit changes to source control in WorkspaceA. You need to ensure that WorkspaceA can be configured for source control. Which two actions should you perform? Each correct answer presents part of the solution. NOTE: Each correct selection is worth one point. From Tenant setting, set Users can synchronize workspace items with their Git repositories to Enabled. From Tenant setting, set Users can sync workspace items with GitHub repositories to Enabled. Configure WorkspaceA to use a Premium Per User (PPU) license. Assign WorkspaceA to Cap1.

You have a Fabric workspace that contains a warehouse named Warehouse1. Warehouse1 contains a table named Customer. Customer contains the following data +------------------+------------------+------------------+--------------------+-----------------------------+ | CustomerID | FirstName | LastName | Phone | CreditCard | +------------------+------------------+------------------+--------------------+-----------------------------+ | 1 | John | Doe | 555-123-4567 | 1234567812345670 | | 2 | Jane | Smith | 555-987-6543 | 8765432187654320 | | 3 | Michael | Johnson | 555-555-5555 | 1234987654321230 | | 4 | Emily | Davis | 555-222-3333 | 4321123456789870 | | 5 | David | Brown | 555-444-5555 | 5678123498761230 | +-------------------+-----------------+-----------------+---------------------+-----------------------------+ You need to provide User1 with access to the Customer table. The solution must prevent User1 from accessing the CreditCard column. How should you complete the statement? To answer, select the appropriate options in the answer area. NOTE: Each correct selection is worth one point.

You have a Fabric deployment pipeline that uses three workspaces named Dev, Test, and Prod. You need to deploy an Eventhouse as part of the deployment process. What should you use to add the Eventhouse to the deployment process?. an Azure DevOps pipeline. an eventstream. GitHub Actions.

You have a Fabric warehouse named DW1. DW1 contains a table that stores sales data and is used by multiple sales representatives. You plan to implement row-level security (RLS). You need to ensure that the sales representatives can see only their respective data. Which warehouse object do you require to implement RLS?. TRIGGER. SCHEMA. FUNCTION. DATABASE ROLE.

You have a Fabric warehouse named DW1. DW1 contains a table that stores sales data and is used by multiple sales representatives. You plan to implement row-level security (RLS). You need to ensure that the sales representatives can see only their respective data. Which warehouse object do you require to implement RLS?. SECURITY POLICY. TABLE. TRIGGER. STORED PROCEDURE.

You have a Fabric F32 capacity that contains a workspace. The workspace contains a warehouse named DW1 that is modelled by using MD5 hash surrogate keys. DW1 contains a single fact table that has grown from 200 million rows to 500 million rows during the past year. You have Microsoft Power BI reports that are based on Direct Lake. The reports show year-over-year values. Users report that the performance of some of the reports has degraded over time and some visuals show errors. You need to resolve the performance issues. The solution must meet the following requirements: •Provide the best query performance. •Minimize operational costs. Which should you do?. Create views. Modify the surrogate keys to use a different data type. Change the MD5 hash to SHA256. Increase the capacity. Disable V-Order on the warehouse.

You have a Fabric workspace named Workspace1 that contains a warehouse named Warehouse1. You plan to deploy Warehouse 1 to a new workspace named Workspace2. As part of the deployment process, you need to verify whether Warehouse1 contains invalid references. The solution must minimize development effort and provide detailed information about the invalid references. What should you use?. a dbt project. a deployment pipeline. a Python script. a database project.

In the Development workspace, you build a new feature named Feature1. You need to deploy Feature1 to the Test workspace. The solution must ensure that only a pipeline is deployed. Which four actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.

You have a Fabric tenant that is linked to a Microsoft Entra tenant. The Microsoft Entra tenant contains a user named User1 and two groups named Group1 and Group2. The Fabric tenant contains the workspaces shown in the following table. Workspace1 • A warehouse for the finance department • Reports for the finance department Workspace2 • A warehouse for the human resources (HR) department • Reports for the HR department • A warehouse for the legal department • Reports for the legal department Workspace3 • A warehouse for the operations department Workspace4 • Reports for the operations department You need to meet the following access requirements: •User1 must have read access to the following: •The operations department reports •The HR department warehouse and reports •The legal department warehouse and reports •User1 must be able to create new items in Workspace2. •Group1 must have access to the finance department warehouse and reports. •Group1 must be able to add new users to the finance department workspace. •Group2 must have access to only the HR warehouse and the legal warehouse. The solution must follow the principle of least privilege. What should you do? To answer, select the appropriate options in the answer area. NOTE: Each correct selection is worth one point.

You have a Fabric workspace named Workspace1 that is connected to a GitHub repository named repo1. Workspace1 contains the items shown in the following table. You modify Semanticmodel1, Semanticmodel2, and Report2. You need to commit the changes to repo1. What is the minimum number of commits you should perform?. 1. 2. 3. 4.

You have a Fabric workspace that contains a lakehouse named Lakehouse1. Lakehouse1 contains a table named Table1. You need to ensure that a user named User1 can view only specific rows in Table1. What should you do first?. Create a security predicate. Create a function. Create a security policy. Grant User1 the SELECT permission for Table1.

You have a Fabric workspace named Workspace1 that contains a lakehouse. You have a Microsoft Entra tenant that contains a user named User1. You need to ensure that User1 can create items in Workspace1. The solution must follow the principle of least privilege. Which workspace role should you assign to User1?. Member. Contributor. Admin. Viewer.

Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution. After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen. You have an Azure key vault named KeyVault1 that contains secrets. You have a Fabric workspace named Workspace1. Workspace contains a notebook named Notebook1 that performs the following tasks: • Loads stage data to the target tables in a lakehouse • Triggers the refresh of a semantic model You plan to add functionality to Notebook1 that will use the Fabric API to monitor the semantic model refreshes. You need to retrieve the registered application ID and secret from KeyVault1 to generate the authentication token. Solution: You use the following code segment: Use notebookutils.credentials.putSecret and specify the key vault URL and the name of a linked service. Does this meet the goal?. Yes. No.

You have a Fabric workspace named Workspace1 that uses version control. Workspace1 and Azure DevOps are integrated. In Azure DevOps, developers create a branch named Branch1 to test extract, transform, and load (ETL) updates. You need to connect Workspace1 to Branch1. The solution must ensure that all the existing content in Branch1 is available in Workspace1. What should you do?. From Workspace1, select Source control, and then select Sync. From Workspace1, select Source control, select Current branch, select Branch1, and then select Commit. From Azure DevOps, merge the contents of the main branch into Branch1. From Workspace1, select Source control, and then select Branch out to new workspace.

You have a Fabric workspace. You plan to deploy two Apache Spark environments that will each contain one of the following notebooks: • Notebook1: A data ingestion notebook that will run as a scheduled Spark job • Notebook2: An ad hoc notebook for interactive data analysis that will be used by a team of analysts and data scientists You need to configure the Spark environments to meet the following requirements: • For the environment that contains Notebook1, minimize compute costs. • For the environment that contains Notebook2, support multiple concurrent users. What should you use for each environment? To answer, select the appropriate options in the answer area. NOTE: Each correct selection is worth one point.

You have a Fabric workspace named Workspace1 that contains a lakehouse named Lakehouse1. You perform the following actions: • Connect Workspace1 to a Git repository and select the main branch. • Create a new branch named feature1 and modify Lakehouse1. • Merge the changes from feature1 into the main branch. You need to ensure that the changes are available in Workspace1. The solution must minimize changes to Workspace1. What should you do?. Disconnect Workspace1 from the Git repository and reconnect Workspace1 to the main branch. From Workspace1, select Source control, and then select Updates. From Workspace1, select Source control, and then select Commit. Switch Workspace1 to the feature1 branch and refresh the workspace.

Case Study - This is a case study. Case studies are not timed separately. You can use as much exam time as you would like to complete each case. However, there may be additional case studies and sections on this exam. You must manage your time to ensure that you are able to complete all questions included on this exam in the time provided. To answer the questions included in a case study, you will need to reference information that is provided in the case study. Case studies might contain exhibits and other resources that provide more information about the scenario that is described in the case study. Each question is independent of the other questions in this case study. At the end of this case study, a review screen will appear. This screen allows you to review your answers and to make changes before you move to the next section of the exam. After you begin a new section, you cannot return to this section. To start the case study - To display the first question in this case study, click the Next button. Use the buttons in the left pane to explore the content of the case study before you answer the questions. Clicking these buttons displays information such as business requirements, existing environment, and problem statements. If the case study has an All Information tab, note that the information displayed is identical to the information displayed on the subsequent tabs. When you are ready to answer a question, click the Question button to return to the question. Overview - Litware, Inc. is a publishing company that has an online bookstore and several retail bookstores worldwide. Litware also manages an online advertising business for the authors it represents. Existing Environment. Fabric Environment Litware has a Fabric workspace named Workspace1. High concurrency is enabled for Workspace1. The company has a data engineering team that uses Python for data processing. Existing Environment. Data Processing The retail bookstores send sales data at the end of each business day, while the online bookstore constantly provides logs and sales data to a central enterprise resource planning (ERP) system. Litware implements a medallion architecture by using the following three layers: bronze, silver, and gold. The sales data is ingested from the ERP system as Parquet files that land in the Files folder in a lakehouse. Notebooks are used to transform the files in a Delta table for the bronze and silver layers. The gold layer is in a warehouse that has V-Order disabled. Litware has image files of book covers in Azure Blob Storage. The files are loaded into the Files folder. Existing Environment. Sales Data Month-end sales data is processed on the first calendar day of each month. Data that is older than one month never changes. In the source system, the sales data refreshes every six hours starting at midnight each day. The sales data is captured in a Dataflow Gen1 dataflow. When the dataflow runs, new and historical data is captured. The dataflow captures the following fields of the source: Sales Date - Author - Price - Units - SKU - A table named AuthorSales stores the sales data that relates to each author. The table contains a column named AuthorEmail. Authors authenticate to a guest Fabric tenant by using their email address. Existing Environment. Security Groups Litware has the following security groups: Sales - Fabric Admins - Streaming Admins - Existing Environment. Performance Issues Business users perform ad-hoc queries against the warehouse. The business users indicate that reports against the warehouse sometimes run for two hours and fail to load as expected. Upon further investigation, the data engineering team receives the following error message when the reports fail to load: “The SQL query failed while running.” The data engineering team wants to debug the issue and find queries that cause more than one failure. When the authors have new book releases, there is often an increase in sales activity. This increase slows the data ingestion process. The company’s sales team reports that during the last month, the sales data has NOT been up-to-date when they arrive at work in the morning. Requirements. Planned Changes - Litware recently signed a contract to receive book reviews. The provider of the reviews exposes the data in Amazon Simple Storage Service (Amazon S3) buckets. Litware plans to manage Search Engine Optimization (SEO) for the authors. The SEO data will be streamed from a REST API. Requirements. Version Control - Litware plans to implement a version control solution in Fabric that will use GitHub integration and follow the principle of least privilege. Requirements. Governance Requirements To control data platform costs, the data platform must use only Fabric services and items. Additional Azure resources must NOT be provisioned. Requirements. Data Requirements - Litware identifies the following data requirements: Process the SEO data in near-real-time (NRT). Make the book reviews available in the lakehouse without making a copy of the data. When a new book cover image arrives in the Files folder, process the image as soon as possible. You need to implement the solution for the book reviews. Which should you do?. Create a Dataflow Gen2 dataflow. Create a shortcut. Enable external data sharing. Create a data pipeline.

Case Study - This is a case study. Case studies are not timed separately. You can use as much exam time as you would like to complete each case. However, there may be additional case studies and sections on this exam. You must manage your time to ensure that you are able to complete all questions included on this exam in the time provided. To answer the questions included in a case study, you will need to reference information that is provided in the case study. Case studies might contain exhibits and other resources that provide more information about the scenario that is described in the case study. Each question is independent of the other questions in this case study. At the end of this case study, a review screen will appear. This screen allows you to review your answers and to make changes before you move to the next section of the exam. After you begin a new section, you cannot return to this section. To start the case study - To display the first question in this case study, click the Next button. Use the buttons in the left pane to explore the content of the case study before you answer the questions. Clicking these buttons displays information such as business requirements, existing environment, and problem statements. If the case study has an All Information tab, note that the information displayed is identical to the information displayed on the subsequent tabs. When you are ready to answer a question, click the Question button to return to the question. Overview - Litware, Inc. is a publishing company that has an online bookstore and several retail bookstores worldwide. Litware also manages an online advertising business for the authors it represents. Existing Environment. Fabric Environment Litware has a Fabric workspace named Workspace1. High concurrency is enabled for Workspace1. The company has a data engineering team that uses Python for data processing. Existing Environment. Data Processing The retail bookstores send sales data at the end of each business day, while the online bookstore constantly provides logs and sales data to a central enterprise resource planning (ERP) system. Litware implements a medallion architecture by using the following three layers: bronze, silver, and gold. The sales data is ingested from the ERP system as Parquet files that land in the Files folder in a lakehouse. Notebooks are used to transform the files in a Delta table for the bronze and silver layers. The gold layer is in a warehouse that has V-Order disabled. Litware has image files of book covers in Azure Blob Storage. The files are loaded into the Files folder. Existing Environment. Sales Data Month-end sales data is processed on the first calendar day of each month. Data that is older than one month never changes. In the source system, the sales data refreshes every six hours starting at midnight each day. The sales data is captured in a Dataflow Gen1 dataflow. When the dataflow runs, new and historical data iscaptured. The dataflow captures the following fields of the source: Sales Date - Author - Price - Units - SKU - A table named AuthorSales stores the sales data that relates to each author. The table contains a column named AuthorEmail. Authors authenticate to a guest Fabric tenant by using their email address. Existing Environment. Security Groups Litware has the following security groups: Sales - Fabric Admins - Streaming Admins - Existing Environment. Performance Issues Business users perform ad-hoc queries against the warehouse. The business users indicate that reports against the warehouse sometimes run for two hours and fail to load as expected. Upon further investigation, the data engineering team receives the following error message when the reports fail to load: “The SQL query failed while running.” The data engineering team wants to debug the issue and find queries that cause more than one failure. When the authors have new book releases, there is often an increase in sales activity. This increase slows the data ingestion process. The company’s sales team reports that during the last month, the sales data has NOT been up-to-date when they arrive at work in the morning. Requirements. Planned Changes - Litware recently signed a contract to receive book reviews. The provider of the reviews exposes the data in Amazon Simple Storage Service (Amazon S3) buckets. Litware plans to manage Search Engine Optimization (SEO) for the authors. The SEO data will be streamed from a REST API. Requirements. Version Control - Litware plans to implement a version control solution in Fabric that will use GitHub integration and follow the principle of least privilege. Requirements. Governance Requirements To control data platform costs, the data platform must use only Fabric services and items. Additional Azure resources must NOT be provisioned. Requirements. Data Requirements - Litware identifies the following data requirements: Process the SEO data in near-real-time (NRT). Make the book reviews available in the lakehouse without making a copy of the data. When a new book cover image arrives in the Files folder, process the image as soon as possible. You need to resolve the sales data issue. The solution must minimize the amount of data transferred. What should you do?. Spilt the dataflow into two dataflows. Configure scheduled refresh for the dataflow. Configure incremental refresh for the dataflow. Set Store rows from the past to 1 Month. Configure incremental refresh for the dataflow. Set Refresh rows from the past to 1 Year. Configure incremental refresh for the dataflow. Set Refresh rows from the past to 1 Month.

Case Study - This is a case study. Case studies are not timed separately. You can use as much exam time as you would like to complete each case. However, there may be additional case studies and sections on this exam. You must manage your time to ensure that you are able to complete all questions included on this exam in the time provided. To answer the questions included in a case study, you will need to reference information that is provided in the case study. Case studies might contain exhibits and other resources that provide more information about the scenario that is described in the case study. Each question is independent of the other questions in this case study. At the end of this case study, a review screen will appear. This screen allows you to review your answers and to make changes before you move to the next section of the exam. After you begin a new section, you cannot return to this section. To start the case study - To display the first question in this case study, click the Next button. Use the buttons in the left pane to explore the content of the case study before you answer the questions. Clicking these buttons displays information such as business requirements, existing environment, and problem statements. If the case study has an All Information tab, note that the information displayed is identical to the information displayed on the subsequent tabs. When you are ready to answer a question, click the Question button to return to the question. Overview. Company Overview - Contoso, Ltd. is an online retail company that wants to modernize its analytics platform by moving to Fabric. The company plans to begin using Fabric for marketing analytics. Overview. IT Structure - The company’s IT department has a team of data analysts and a team of data engineers that use analytics systems. The data engineers perform the ingestion, transformation, and loading of data. They prefer to use Python or SQL to transform the data. The data analysts query data and create semantic models and reports. They are qualified to write queries in Power Query and T-SQL. Existing Environment. Fabric - Contoso has an F64 capacity named Cap1. All Fabric users are allowed to create items. Contoso has two workspaces named WorkspaceA and WorkspaceB that currently use Pro license mode. Existing Environment. Source Systems Contoso has a point of sale (POS) system named POS1 that uses an instance of SQL Server on Azure Virtual Machines in the same Microsoft Entra tenant as Fabric. The host virtual machine is on a private virtual network that has public access blocked. POS1 contains all the sales transactions that were processed on the company’s website. The company has a software as a service (SaaS) online marketing app named MAR1. MAR1 has seven entities. The entities contain data that relates to email open rates and interaction rates, as well as website interactions. The data can be exported from MAR1 by calling REST APIs. Each entity has a different endpoint. Contoso has been using MAR1 for one year. Data from prior years is stored in Parquet files in an Amazon Simple Storage Service (Amazon S3) bucket. There are 12 files that range in size from 300 MB to 900 MB and relate to email interactions. Existing Environment. Product Data POS1 contains a product list and related data. The data comes from the following three tables: Products - ProductCategories - ProductSubcategories - In the data, products are related to product subcategories, and subcategories are related to product categories. Existing Environment. Azure - Contoso has a Microsoft Entra tenant that has the following mail-enabled security groups: DataAnalysts: Contains the data analysts DataEngineers: Contains the data engineers Contoso has an Azure subscription. The company has an existing Azure DevOps organization and creates a new project for repositories that relate to Fabric. Existing Environment. User Problems The VP of marketing at Contoso requires analysis on the effectiveness of different types of email content. It typically takes a week to manually compile and analyze the data. Contoso wants to reduce the time to less than one day by using Fabric. The data engineering team has successfully exported data from MAR1. The team experiences transient connectivity errors, which causes the data exports to fail. Requirements. Planned Changes - Contoso plans to create the following two lakehouses: Lakehouse1: Will store both raw and cleansed data from the sources Lakehouse2: Will serve data in a dimensional model to users for analytical queries Additional items will be added to facilitate data ingestion and transformation. Contoso plans to use Azure Repos for source control in Fabric. Requirements. Technical Requirements The new lakehouses must follow a medallion architecture by using the following three layers: bronze, silver, and gold. There will be extensive data cleansing required to populate the MAR1 data in the silver layer, including deduplication, the handling of missing values, and the standardizing of capitalization. Each layer must be fully populated before moving on to the next layer. If any step in populating the lakehouses fails, an email must be sent to the data engineers. Data imports must run simultaneously, when possible. The use of email data from the Amazon S3 bucket must meet the following requirements: Minimize egress costs associated with cross-cloud data access. Prevent saving a copy of the raw data in the lakehouses. Items that relate to data ingestion must meet the following requirements: The items must be source controlled alongside other workspace items. Ingested data must land in the bronze layer of Lakehouse1 in the Delta format. No changes other than changes to the file formats must be implemented before the data lands in the bronze layer. Development effort must be minimized and a built-in connection must be used to import the source data. In the event of a connectivity error, the ingestion processes must attempt the connection again. Lakehouses, data pipelines, and notebooks must be stored in WorkspaceA. Semantic models, reports, and dataflows must be stored in WorkspaceB. Once a week, old files that are no longer referenced by a Delta table log must be removed. Requirements. Data Transformation In the POS1 product data, ProductID values are unique. The product dimension in the gold layer must include only active products from product list. Active products are identified by an IsActive value of 1. Some product categories and subcategories are NOT assigned to any product. They are NOT analytically relevant and must be omitted from the product dimension in the gold layer. Requirements. Data Security - Security in Fabric must meet the following requirements: The data engineers must have read and write access to all the lakehouses, including the underlying files. The data analysts must only have read access to the Delta tables in the gold layer. The data analysts must NOT have access to the data in the bronze and silver layers. The data engineers must be able to commit changes to source control in WorkspaceA. You need to recommend a method to populate the POS1 data to the lakehouse medallion layers. What should you recommend for each layer? To answer, select the appropriate options in the answer area. NOTE: Each correct selection is worth one point.

To start the case study - To display the first question in this case study, click the Next button. Use the buttons in the left pane to explore the content of the case study before you answer the questions. Clicking these buttons displays information such as business requirements, existing environment, and problem statements. If the case study has an All Information tab, note that the information displayed is identical to the information displayed on the subsequent tabs. When you are ready to answer a question, click the Question button to return to the question. Overview. Company Overview - Contoso, Ltd. is an online retail company that wants to modernize its analytics platform by moving to Fabric. The company plans to begin using Fabric for marketing analytics. Overview. IT Structure - The company’s IT department has a team of data analysts and a team of data engineers that use analytics systems. The data engineers perform the ingestion, transformation, and loading of data. They prefer to use Python or SQL to transform the data. The data analysts query data and create semantic models and reports. They are qualified to write queries in Power Query and T-SQL. Existing Environment. Fabric - Contoso has an F64 capacity named Cap1. All Fabric users are allowed to create items. Contoso has two workspaces named WorkspaceA and WorkspaceB that currently use Pro license mode. Existing Environment. Source Systems Contoso has a point of sale (POS) system named POS1 that uses an instance of SQL Server on Azure Virtual Machines in the same Microsoft Entra tenant as Fabric. The host virtual machine is on a private virtual network that has public access blocked. POS1 contains all the sales transactions that were processed on the company’s website. The company has a software as a service (SaaS) online marketing app named MAR1. MAR1 has seven entities. The entities contain data that relates to email open rates and interaction rates, as well as website interactions. The data can be exported from MAR1 by calling REST APIs. Each entity has a different endpoint. Contoso has been using MAR1 for one year. Data from prior years is stored in Parquet files in an Amazon Simple Storage Service (Amazon S3) bucket. There are 12 files that range in size from 300 MB to 900 MB and relate to email interactions. Existing Environment. Product Data POS1 contains a product list and related data. The data comes from the following three tables: Products - ProductCategories - ProductSubcategories - In the data, products are related to product subcategories, and subcategories are related to product categories. Existing Environment. Azure - Contoso has a Microsoft Entra tenant that has the following mail-enabled security groups: DataAnalysts: Contains the data analysts DataEngineers: Contains the data engineers Contoso has an Azure subscription. The company has an existing Azure DevOps organization and creates a new project for repositories that relate to Fabric. Existing Environment. User Problems The VP of marketing at Contoso requires analysis on the effectiveness of different types of email content. It typically takes a week to manually compile and analyze the data. Contoso wants to reduce the time to less than one day by using Fabric. The data engineering team has successfully exported data from MAR1. The team experiences transient connectivity errors, which causes the data exports to fail. Requirements. Planned Changes - Contoso plans to create the following two lakehouses: Lakehouse1: Will store both raw and cleansed data from the sources Lakehouse2: Will serve data in a dimensional model to users for analytical queries Additional items will be added to facilitate data ingestion and transformation. Contoso plans to use Azure Repos for source control in Fabric. Requirements. Technical Requirements The new lakehouses must follow a medallion architecture by using the following three layers: bronze, silver, and gold. There will be extensive data cleansing required to populate the MAR1 data in the silver layer, including deduplication, the handling of missing values, and the standardizing of capitalization. Each layer must be fully populated before moving on to the next layer. If any step in populating the lakehouses fails, an email must be sent to the data engineers. Data imports must run simultaneously, when possible. The use of email data from the Amazon S3 bucket must meet the following requirements: Minimize egress costs associated with cross-cloud data access. Prevent saving a copy of the raw data in the lakehouses. Items that relate to data ingestion must meet the following requirements: The items must be source controlled alongside other workspace items. Ingested data must land in the bronze layer of Lakehouse1 in the Delta format. No changes other than changes to the file formats must be implemented before the data lands in the bronze layer. Development effort must be minimized and a built-in connection must be used to import the source data. In the event of a connectivity error, the ingestion processes must attempt the connection again. Lakehouses, data pipelines, and notebooks must be stored in WorkspaceA. Semantic models, reports, and dataflows must be stored in WorkspaceB. Once a week, old files that are no longer referenced by a Delta table log must be removed. Requirements. Data Transformation In the POS1 product data, ProductID values are unique. The product dimension in the gold layer must include only active products from product list. Active products are identified by an IsActive value of 1. Some product categories and subcategories are NOT assigned to any product. They are NOT analytically relevant and must be omitted from the product dimension in the gold layer. Requirements. Data Security - Security in Fabric must meet the following requirements: The data engineers must have read and write access to all the lakehouses, including the underlying files. The data analysts must only have read access to the Delta tables in the gold layer. The data analysts must NOT have access to the data in the bronze and silver layers. The data engineers must be able to commit changes to source control in WorkspaceA. You need to ensure that usage of the data in the Amazon S3 bucket meets the technical requirements. What should you do?. Create a workspace identity and enable high concurrency for the notebooks. Create a shortcut and ensure that caching is disabled for the workspace. Create a workspace identity and use the identity in a data pipeline. Create a shortcut and ensure that caching is enabled for the workspace.

Case Study - This is a case study. Case studies are not timed separately. You can use as much exam time as you would like to complete each case. However, there may be additional case studies and sections on this exam. You must manage your time to ensure that you are able to complete all questions included on this exam in the time provided. To answer the questions included in a case study, you will need to reference information that is provided in the case study. Case studies might contain exhibits and other resources that provide more information about the scenario that is described in the case study. Each question is independent of the other questions in this case study. At the end of this case study, a review screen will appear. This screen allows you to review your answers and to make changes before you move to the next section of the exam. After you begin a new section, you cannot return to this section. To start the case study - To display the first question in this case study, click the Next button. Use the buttons in the left pane to explore the content of the case study before you answer the questions. Clicking these buttons displays information such as business requirements, existing environment, and problem statements. If the case study has an All Information tab, note that the information displayed is identical to the information displayed on the subsequent tabs. When you are ready to answer a question, click the Question button to return to the question. Overview. Company Overview - Contoso, Ltd. is an online retail company that wants to modernize its analytics platform by moving to Fabric. The company plans to begin using Fabric for marketing analytics. Overview. IT Structure - The company’s IT department has a team of data analysts and a team of data engineers that use analytics systems. The data engineers perform the ingestion, transformation, and loading of data. They prefer to use Python or SQL to transform the data. The data analysts query data and create semantic models and reports. They are qualified to write queries in Power Query and T-SQL. Existing Environment. Fabric - Contoso has an F64 capacity named Cap1. All Fabric users are allowed to create items. Contoso has two workspaces named WorkspaceA and WorkspaceB that currently use Pro license mode. Existing Environment. Source Systems Contoso has a point of sale (POS) system named POS1 that uses an instance of SQL Server on Azure Virtual Machines in the same Microsoft Entra tenant as Fabric. The host virtual machine is on a private virtual network that has public access blocked. POS1 contains all the sales transactions that were processed on the company’s website. The company has a software as a service (SaaS) online marketing app named MAR1. MAR1 has seven entities. The entities contain data that relates to email open rates and interaction rates, as well as website interactions. The data can be exported from MAR1 by calling REST APIs. Each entity has a different endpoint. Contoso has been using MAR1 for one year. Data from prior years is stored in Parquet files in an Amazon Simple Storage Service (Amazon S3) bucket. There are 12 files that range in size from 300 MB to 900 MB and relate to email interactions. Existing Environment. Product Data POS1 contains a product list and related data. The data comes from the following three tables: Products - ProductCategories - ProductSubcategories - In the data, products are related to product subcategories, and subcategories are related to product categories. Existing Environment. Azure - Contoso has a Microsoft Entra tenant that has the following mail-enabled security groups: DataAnalysts: Contains the data analysts DataEngineers: Contains the data engineers Contoso has an Azure subscription. The company has an existing Azure DevOps organization and creates a new project for repositories that relate to Fabric. Existing Environment. User Problems The VP of marketing at Contoso requires analysis on the effectiveness of different types of email content. It typically takes a week to manually compile and analyze the data. Contoso wants to reduce the time to less than one day by using Fabric. The data engineering team has successfully exported data from MAR1. The team experiences transient connectivity errors, which causes the data exports to fail. Requirements. Planned Changes - Contoso plans to create the following two lakehouses: Lakehouse1: Will store both raw and cleansed data from the sources Lakehouse2: Will serve data in a dimensional model to users for analytical queries Additional items will be added to facilitate data ingestion and transformation. Contoso plans to use Azure Repos for source control in Fabric. Requirements. Technical Requirements The new lakehouses must follow a medallion architecture by using the following three layers: bronze, silver, and gold. There will be extensive data cleansing required to populate the MAR1 data in the silver layer, including deduplication, the handling of missing values, and the standardizing of capitalization. Each layer must be fully populated before moving on to the next layer. If any step in populating the lakehouses fails, an email must be sent to the data engineers. Data imports must run simultaneously, when possible. The use of email data from the Amazon S3 bucket must meet the following requirements: Minimize egress costs associated with cross-cloud data access. Prevent saving a copy of the raw data in the lakehouses. Items that relate to data ingestion must meet the following requirements: The items must be source controlled alongside other workspace items. Ingested data must land in the bronze layer of Lakehouse1 in the Delta format. No changes other than changes to the file formats must be implemented before the data lands in the bronze layer. Development effort must be minimized and a built-in connection must be used to import the source data. In the event of a connectivity error, the ingestion processes must attempt the connection again. Lakehouses, data pipelines, and notebooks must be stored in WorkspaceA. Semantic models, reports, and dataflows must be stored in WorkspaceB. Once a week, old files that are no longer referenced by a Delta table log must be removed. Requirements. Data Transformation In the POS1 product data, ProductID values are unique. The product dimension in the gold layer must include only active products from product list. Active products are identified by an IsActive value of 1. Some product categories and subcategories are NOT assigned to any product. They are NOT analytically relevant and must be omitted from the product dimension in the gold layer. Requirements. Data Security - Security in Fabric must meet the following requirements: The data engineers must have read and write access to all the lakehouses, including the underlying files. The data analysts must only have read access to the Delta tables in the gold layer. The data analysts must NOT have access to the data in the bronze and silver layers. The data engineers must be able to commit changes to source control in WorkspaceA. You need to create the product dimension. How should you complete the Apache Spark SQL code? To answer, select the appropriate options in the answer area. NOTE: Each correct selection is worth one point.

Case Study - This is a case study. Case studies are not timed separately. You can use as much exam time as you would like to complete each case. However, there may be additional case studies and sections on this exam. You must manage your time to ensure that you are able to complete all questions included on this exam in the time provided. To answer the questions included in a case study, you will need to reference information that is provided in the case study. Case studies might contain exhibits and other resources that provide more information about the scenario that is described in the case study. Each question is independent of the other questions in this case study. At the end of this case study, a review screen will appear. This screen allows you to review your answers and to make changes before you move to the next section of the exam. After you begin a new section, you cannot return to this section. To start the case study - To display the first question in this case study, click the Next button. Use the buttons in the left pane to explore the content of the case study before you answer the questions. Clicking these buttons displays information such as business requirements, existing environment, and problem statements. If the case study has an All Information tab, note that the information displayed is identical to the information displayed on the subsequent tabs. When you are ready to answer a question, click the Question button to return to the question. Overview. Company Overview - Contoso, Ltd. is an online retail company that wants to modernize its analytics platform by moving to Fabric. The company plans to begin using Fabric for marketing analytics. Overview. IT Structure - The company’s IT department has a team of data analysts and a team of data engineers that use analytics systems. The data engineers perform the ingestion, transformation, and loading of data. They prefer to use Python or SQL to transform the data. The data analysts query data and create semantic models and reports. They are qualified to write queries in Power Query and T-SQL. Existing Environment. Fabric - Contoso has an F64 capacity named Cap1. All Fabric users are allowed to create items. Contoso has two workspaces named WorkspaceA and WorkspaceB that currently use Pro license mode. Existing Environment. Source Systems Contoso has a point of sale (POS) system named POS1 that uses an instance of SQL Server on Azure Virtual Machines in the same Microsoft Entra tenant as Fabric. The host virtual machine is on a private virtual network that has public access blocked. POS1 contains all the sales transactions that were processed on the company’s website. The company has a software as a service (SaaS) online marketing app named MAR1. MAR1 has seven entities. The entities contain data that relates to email open rates and interaction rates, as well as website interactions. The data can be exported from MAR1 by calling REST APIs. Each entity has a different endpoint. Contoso has been using MAR1 for one year. Data from prior years is stored in Parquet files in an Amazon Simple Storage Service (Amazon S3) bucket. There are 12 files that range in size from 300 MB to 900 MB and relate to email interactions. Existing Environment. Product Data POS1 contains a product list and related data. The data comes from the following three tables: Products - ProductCategories - ProductSubcategories - In the data, products are related to product subcategories, and subcategories are related to product categories. Existing Environment. Azure - Contoso has a Microsoft Entra tenant that has the following mail-enabled security groups: DataAnalysts: Contains the data analysts DataEngineers: Contains the data engineers Contoso has an Azure subscription. The company has an existing Azure DevOps organization and creates a new project for repositories that relate to Fabric. Existing Environment. User Problems The VP of marketing at Contoso requires analysis on the effectiveness of different types of email content. It typically takes a week to manually compile and analyze the data. Contoso wants to reduce the time to less than one day by using Fabric. The data engineering team has successfully exported data from MAR1. The team experiences transient connectivity errors, which causes the data exports to fail. Requirements. Planned Changes - Contoso plans to create the following two lakehouses: Lakehouse1: Will store both raw and cleansed data from the sources Lakehouse2: Will serve data in a dimensional model to users for analytical queries Additional items will be added to facilitate data ingestion and transformation. Contoso plans to use Azure Repos for source control in Fabric. Requirements. Technical Requirements The new lakehouses must follow a medallion architecture by using the following three layers: bronze, silver, and gold. There will be extensive data cleansing required to populate the MAR1 data in the silver layer, including deduplication, the handling of missing values, and the standardizing of capitalization. Each layer must be fully populated before moving on to the next layer. If any step in populating the lakehouses fails, an email must be sent to the data engineers. Data imports must run simultaneously, when possible. The use of email data from the Amazon S3 bucket must meet the following requirements: Minimize egress costs associated with cross-cloud data access. Prevent saving a copy of the raw data in the lakehouses. Items that relate to data ingestion must meet the following requirements: The items must be source controlled alongside other workspace items. Ingested data must land in the bronze layer of Lakehouse1 in the Delta format. No changes other than changes to the file formats must be implemented before the data lands in the bronze layer. Development effort must be minimized and a built-in connection must be used to import the source data. In the event of a connectivity error, the ingestion processes must attempt the connection again. Lakehouses, data pipelines, and notebooks must be stored in WorkspaceA. Semantic models, reports, and dataflows must be stored in WorkspaceB. Once a week, old files that are no longer referenced by a Delta table log must be removed. Requirements. Data Transformation In the POS1 product data, ProductID values are unique. The product dimension in the gold layer must include only active products from product list. Active products are identified by an IsActive value of 1. Some product categories and subcategories are NOT assigned to any product. They are NOT analytically relevant and must be omitted from the product dimension in the gold layer. Requirements. Data Security - Security in Fabric must meet the following requirements: The data engineers must have read and write access to all the lakehouses, including the underlying files. The data analysts must only have read access to the Delta tables in the gold layer. The data analysts must NOT have access to the data in the bronze and silver layers. The data engineers must be able to commit changes to source control in WorkspaceA. You need to populate the MAR1 data in the bronze layer. Which two types of activities should you include in the pipeline? Each correct answer presents part of the solution. NOTE: Each correct selection is worth one point. ForEach. Copy data. WebHook. Stored procedure.

You have a Fabric workspace that contains a warehouse named Warehouse1. Warehouse1 contains the following tables and columns. --IMAGEN You need to denormalize the tables and include the ContractType and StartDate columns in the Employee table. The solution must meet the following requirements: Ensure that the StartDate column is of the date data type. Ensure that all the rows from the Employee table are preserved and include any matching rows from the Contract table. Ensure that the result set displays the total number of employees per contract type for all the contract types that have more than two employees. How should you complete the statement? To answer, select the appropriate options in the answer area. NOTE: Each correct selection is worth one point.

HOTSPOT - You have an Azure Event Hubs data source that contains weather data. You ingest the data from the data source by using an eventstream named Eventstream1. Eventstream1 uses a lakehouse as the destination. You need to batch ingest only rows from the data source where the City attribute has a value of Kansas. The filter must be added before the destination. The solution must minimize development effort. What should you use for the data processor and filtering? To answer, select the appropriate options in the answer area. NOTE: Each correct selection is worth one point.

You have a Fabric workspace that contains an eventstream named Eventstream1. Eventstream1 processes data from a thermal sensor by using event stream processing, and then stores the data in a lakehouse. You need to modify Eventstream1 to include the standard deviation of the temperature. Which transform operator should you include in the Eventstream1 logic?. Expand. Group by. Union. Aggregate.

You have an Azure event hub. Each event contains the following fields: BikepointID - Street - Neighbourhood - Latitude - Longitude - No_Bikes - No_Empty_Docks - You need to ingest the events. The solution must only retain events that have a Neighbourhood value of Chelsea, and then store the retained events in a Fabric lakehouse. What should you use?. a KQL queryset. an eventstream. a streaming dataset. Apache Spark Structured Streaming.

You are building a data loading pattern for Fabric notebook workloads. You have the following code segment: def loading_pattern_sample(df_source): try: deltaTable = DeltaTable.forName(spark, target_table) except Exception: try: df_source.write.format('delta').mode('overwrite').saveAsTable(f"{target_table}") except Exception as e: print(f'Load for table {target_table} failed with error: {str(e)}') raise return try: change_detection_columns = [col for col in df_source.columns if col not in candidate_key] match_condition = ' AND '.join([f'target.{col} = source.{col}' for col in candidate_key]) update_condition = ' OR '.join([f'target.{col} != source.{col}' for col in change_detection_columns]) update_expr = {col: f'source.{col}' for col in df_source.columns} merge_operation = deltaTable.alias('target').merge( source=df_source.alias('source'), condition=match_condition ).whenMatchedUpdate( condition=update_condition, set=update_expr ).whenNotMatchedInsertAll() merge_operation.execute() except Exception as e: print(f'Insert operation for table {target_table} failed with error: {str(e)}') return.

You have a Fabric workspace that contains two lakehouses named Lakehouse1 and Lakehouse2. Lakehouse1 contains staging data in a Delta table named Orderlines. Lakehouse2 contains a Type 2 slowly changing dimension (SCD) dimension table named Dim_Customer. You need to build a query that will combine data from Orderlines and Dim_Customer to create a new fact table named Fact_Orders. The new table must meet the following requirements: Enable the analysis of customer orders based on historical attributes. Enable the analysis of customer orders based on the current attributes. How should you complete the statement? To answer, select the appropriate options in the answer area. NOTE: Each correct selection is worth one point. SELECT OrderLineID order_line_id ,OrderDate order_date ,c.customer_key ,c.customer_id ,Quantity order_quantity ,UnitPrice unit_price ,TaxRate tax_rate FROM Lakehouse1.orderlines o INNER JOIN Lakehouse2.dim_customer c ON o.customerid = c.customer_id.