ML Ops

A company plans to build a custom natural language processing (NLP) model to classify and prioritize user feedback. The company hosts the data and all machine learning (ML) infrastructure in the AWS Cloud. The ML team works from the company's office, which has an IPsec VPN connection to one VPC in the AWS Cloud. The company has set both the enableDnsHostnames attribute and the enableDnsSupport attribute of the VPC to true. The company's DNS resolvers point to the VPC DNS. The company does not allow the ML team to access Amazon SageMaker notebooks through connections that use the public internet. The connection must stay within a private network and within the AWS internal network. Which solution will meet these requirements with the LEAST development effort?. Create a VPC interface endpoint for the SageMaker notebook in the VPC. Access the notebook through a VPN connection and the VPC endpoint. Create a bastion host by using Amazon EC2 in a public subnet within the VPC. Log in to the bastion host through a VPN connection. Access the SageMaker notebook from the bastion host. Create a bastion host by using Amazon EC2 in a private subnet within the VPC with a NAT gateway. Log in to the bastion host through a VPN connection. Access the SageMaker notebook from the bastion host. Create a NAT gateway in the VPC. Access the SageMaker notebook HTTPS endpoint through a VPN connection and the NAT gateway.

A data scientist is using Amazon Comprehend to perform sentiment analysis on a dataset of one million social media posts. Which approach will process the dataset in the LEAST time?. Use a combination of AWS Step Functions and an AWS Lambda function to call the DetectSentiment API operation for each post synchronously. Use a combination of AWS Step Functions and an AWS Lambda function to call the BatchDetectSentiment API operation with batches of up to 25 posts at a time. Upload the posts to Amazon S3. Pass the S3 storage path to an AWS Lambda function that calls the StartSentimentDetectionJob API operation. Use an AWS Lambda function to call the BatchDetectSentiment API operation with the whole dataset.

A machine learning (ML) specialist at a retail company must build a system to forecast the daily sales for one of the company's stores. The company provided the ML specialist with sales data for this store from the past 10 years. The historical dataset includes the total amount of sales on each day for the store. Approximately 10% of the days in the historical dataset are missing sales data. The ML specialist builds a forecasting model based on the historical dataset. The specialist discovers that the model does not meet the performance standards that the company requires. Which action will MOST likely improve the performance for the forecasting model?. Aggregate sales from stores in the same geographic area. Apply smoothing to correct for seasonal variation. Change the forecast frequency from daily to weekly. Replace missing values in the dataset by using linear interpolation.

A mining company wants to use machine learning (ML) models to identify mineral images in real time. A data science team built an image recognition model that is based on convolutional neural network (CNN). The team trained the model on Amazon SageMaker by using GPU instances. The team will deploy the model to a SageMaker endpoint. The data science team already knows the workload traffic patterns. The team must determine instance type and configuration for the workloads. Which solution will meet these requirements with the LEAST development effort?. Register the model artifact and container to the SageMaker Model Registry. Use the SageMaker Inference Recommender Default job type. Provide the known traffic pattern for load testing to select the best instance type and configuration based on the workloads. Register the model artifact and container to the SageMaker Model Registry. Use the SageMaker Inference Recommender Advanced job type. Provide the known traffic pattern for load testing to select the best instance type and configuration based on the workloads. Deploy the model to an endpoint by using GPU instances. Use AWS Lambda and Amazon API Gateway to handle invocations from the web. Use open-source tools to perform load testing against the endpoint and to select the best instance type and configuration. Deploy the model to an endpoint by using CPU instances. Use AWS Lambda and Amazon API Gateway to handle invocations from the web. Use open-source tools to perform load testing against the endpoint and to select the best instance type and configuration.

A company is building custom deep learning models in Amazon SageMaker by using training and inference containers that run on Amazon EC2 instances. The company wants to reduce training costs but does not want to change the current architecture. The SageMaker training job can finish after interruptions. The company can wait days for the results. Which combination of resources should the company use to meet these requirements MOST cost-effectively? (Choose two.). On-Demand Instances. Checkpoints. Reserved Instances. Incremental training. Spot instanc.

A company hosts a public web application on AWS. The application provides a user feedback feature that consists of free-text fields where users can submit text to provide feedback. The company receives a large amount of free-text user feedback from the online web application. The product managers at the company classify the feedback into a set of fixed categories including user interface issues, performance issues, new feature request, and chat issues for further actions by the company's engineering teams. A machine learning (ML) engineer at the company must automate the classification of new user feedback into these fixed categories by using Amazon SageMaker. A large set of accurate data is available from the historical user feedback that the product managers previously classified. Which solution should the ML engineer apply to perform multi-class text classification of the user feedback?. Use the SageMaker Latent Dirichlet Allocation (LDA) algorithm. Use the SageMaker BlazingText algorithm. Use the SageMaker Neural Topic Model (NTM) algorithm. Use the SageMaker CatBoost algorithm.

A digital media company wants to build a customer churn prediction model by using tabular data. The model should clearly indicate whether a customer will stop using the company's services. The company wants to clean the data because the data contains some empty fields, duplicate values, and rare values. Which solution will meet these requirements with the LEAST development effort?. Use SageMaker Canvas to automatically clean the data and to prepare a categorical model. Use SageMaker Data Wrangler to clean the data. Use the built-in SageMaker XGBoost algorithm to train a classification model. Use SageMaker Canvas automatic data cleaning and preparation tools. Use the built-in SageMaker XGBoost algorithm to train a regression model. Use SageMaker Data Wrangler to clean the data. Use the SageMaker Autopilot to train a regression model Suggested Answer:.

A data engineer is evaluating customer data in Amazon SageMaker Data Wrangler. The data engineer will use the customer data to create a new model to predict customer behavior. The engineer needs to increase the model performance by checking for multicollinearity in the dataset. Which steps can the data engineer take to accomplish this with the LEAST operational effort? (Choose two.). Use SageMaker Data Wrangler to refit and transform the dataset by applying one-hot encoding to category-based variables. Use SageMaker Data Wrangler diagnostic visualization. Use principal components analysis (PCA) and singular value decomposition (SVD) to calculate singular values. Use the SageMaker Data Wrangler Quick Model visualization to quickly evaluate the dataset and to produce importance scores for each feature. Use the SageMaker Data Wrangler Min Max Scaler transform to normalize the data. Use SageMaker Data Wrangler diagnostic visualization. Use least absolute shrinkage and selection operator (LASSO) to plot coefficient values from a LASSO model that is trained on the dataset.

A company processes millions of orders every day. The company uses Amazon DynamoDB tables to store order information. When customers submit new orders, the new orders are immediately added to the DynamoDB tables. New orders arrive in the DynamoDB tables continuously. A data scientist must build a peak-time prediction solution. The data scientist must also create an Amazon QuickSight dashboard to display near real-time order insights. The data scientist needs to build a solution that will give QuickSight access to the data as soon as new order information arrives. Which solution will meet these requirements with the LEAST delay between when a new order is processed and when QuickSight can access the new order information?. Use AWS Glue to export the data from Amazon DynamoDB to Amazon S3. Configure QuickSight to access the data in Amazon S3. Use Amazon Kinesis Data Streams to export the data from Amazon DynamoDB to Amazon S3. Configure QuickSight to access the data in Amazon S3. Use an API call from QuickSight to access the data that is in Amazon DynamoDB directly. Use Amazon Kinesis Data Firehose to export the data from Amazon DynamoDB to Amazon S3. Configure QuickSight to access the data in Amazon S3.

A data engineer is preparing a dataset that a retail company will use to predict the number of visitors to stores. The data engineer created an Amazon S3 bucket. The engineer subscribed the S3 bucket to an AWS Data Exchange data product for general economic indicators. The data engineer wants to join the economic indicator data to an existing table in Amazon Athena to merge with the business data. All these transformations must finish running in 30-60 minutes. Which solution will meet these requirements MOST cost-effectively?. Configure the AWS Data Exchange product as a producer for an Amazon Kinesis data stream. Use an Amazon Kinesis Data Firehose delivery stream to transfer the data to Amazon S3. Run an AWS Glue job that will merge the existing business data with the Athena table. Write the result set back to Amazon S3. Use an S3 event on the AWS Data Exchange S3 bucket to invoke an AWS Lambda function. Program the Lambda function to use Amazon SageMaker Data Wrangler to merge the existing business data with the Athena table. Write the result set back to Amazon S3. Use an S3 event on the AWS Data Exchange S3 bucket to invoke an AWS Lambda function. Program the Lambda function to run an AWS Glue job that will merge the existing business data with the Athena table. Write the results back to Amazon S3. Provision an Amazon Redshift cluster. Subscribe to the AWS Data Exchange product and use the product to create an Amazon Redshift table. Merge the data in Amazon Redshift. Write the results back to Amazon S3.

A company operates large cranes at a busy port The company plans to use machine learning (ML) for predictive maintenance of the cranes to avoid unexpected breakdowns and to improve productivity. The company already uses sensor data from each crane to monitor the health of the cranes in real time. The sensor data includes rotation speed, tension, energy consumption, vibration, pressure, and temperature for each crane. The company contracts AWS ML experts to implement an ML solution. Which potential findings would indicate that an ML-based solution is suitable for this scenario? (Choose two.). The historical sensor data does not include a significant number of data points and attributes for certain time periods. The historical sensor data shows that simple rule-based thresholds can predict crane failures. The historical sensor data contains failure data for only one type of crane model that is in operation and lacks failure data of most other types of crane that are in operation. The historical sensor data from the cranes are available with high granularity for the last 3 years. The historical sensor data contains most common types of crane failures that the company wants to predict.

A company wants to create an artificial intelligence (AШ) yoga instructor that can lead large classes of students. The company needs to create a feature that can accurately count the number of students who are in a class. The company also needs a feature that can differentiate students who are performing a yoga stretch correctly from students who are performing a stretch incorrectly. Determine whether students are performing a stretch correctly, the solution needs to measure the location and angle of each student’s arms and legs. A data scientist must use Amazon SageMaker to access video footage of a yoga class by extracting image frames and applying computer vision models. Which combination of models will meet these requirements with the LEAST effort? (Choose two.). Image Classification. Optical Character Recognition (OCR). Object Detection. Pose estimation. Image Generative Adversarial Networks (GANs).

An ecommerce company has used Amazon SageMaker to deploy a factorization machines (FM) model to suggest products for customers. The company’s data science team has developed two new models by using the TensorFlow and PyTorch deep learning frameworks. The company needs to use A/B testing to evaluate the new models against the deployed model. The required A/B testing setup is as follows: • Send 70% of traffic to the FM model, 15% of traffic to the TensorFlow model, and 15% of traffic to the PyTorch model. • For customers who are from Europe, send all traffic to the TensorFlow model. Which architecture can the company use to implement the required A/B testing setup?. Create two new SageMaker endpoints for the TensorFlow and PyTorch models in addition to the existing SageMaker endpoint. Create an Application Load Balancer. Create a target group for each endpoint. Configure listener rules and add weight to the target groups. To send traffic to the TensorFlow model for customers who are from Europe, create an additional listener rule to forward traffic to the TensorFlow target group. Create two production variants for the TensorFlow and PyTorch models. Create an auto scaling policy and configure the desired A/B weights to direct traffic to each production variant. Update the existing SageMaker endpoint with the auto scaling policy. To send traffic to the TensorFlow model for customers who are from Europe, set the TargetVariant header in the request to point to the variant name of the TensorFlow model. Create two new SageMaker endpoints for the TensorFlow and PyTorch models in addition to the existing SageMaker endpoint. Create a Network Load Balancer. Create a target group for each endpoint. Configure listener rules and add weight to the target groups. To send traffic to the TensorFlow model for customers who are from Europe, create an additional listener rule to forward traffic to the TensorFlow target group. Create two production variants for the TensorFlow and PyTorch models. Specify the weight for each production variant in the SageMaker endpoint configuration. Update the existing SageMaker endpoint with the new configuration. To send traffic to the TensorFlow model for customers who are from Europe, set the TargetVariant header in the request to point to the variant name of the TensorFlow model. Most Voted.

A data scientist stores financial datasets in Amazon S3. The data scientist uses Amazon Athena to query the datasets by using SQL. The data scientist uses Amazon SageMaker to deploy a machine learning (ML) model. The data scientist wants to obtain inferences from the model at the SageMaker endpoint. However, when the data scientist attempts to invoke the SageMaker endpoint, the data scientist receives SQL statement failures. The data scientist’s IAM user is currently unable to invoke the SageMaker endpoint. Which combination of actions will give the data scientist’s IAM user the ability to invoke the SageMaker endpoint? (Choose three.). Attach the AmazonAthenaFullAccess AWS managed policy to the user identity. Include a policy statement for the data scientist's IAM user that allows the IAM user to perform the sagemaker:InvokeEndpoint action. Include an inline policy for the data scientist’s IAM user that allows SageMaker to read S3 objects. Most Voted. Include a policy statement for the data scientist’s IAM user that allows the IAM user to perform the sagemaker:GetRecord action. Include the SQL statement "USING EXTERNAL FUNCTION ml_function_name'' in the Athena SQL query. Most Voted. Perform a user remapping in SageMaker to map the IAM user to another IAM user that is on the hosted endpoint.

A data scientist is building a linear regression model. The scientist inspects the dataset and notices that the mode of the distribution is lower than the median, and the median is lower than the mean. Which data transformation will give the data scientist the ability to apply a linear regression model?. Exponential transformation. Logarithmic transformation. Polynomial transformation. Sinusoidal transformation.

A data scientist receives a collection of insurance claim records. Each record includes a claim ID. the final outcome of the insurance claim, and the date of the final outcome. The final outcome of each claim is a selection from among 200 outcome categories. Some claim records include only partial information. However, incomplete claim records include only 3 or 4 outcome categories from among the 200 available outcome categories. The collection includes hundreds of records for each outcome category. The records are from the previous 3 years. The data scientist must create a solution to predict the number of claims that will be in each outcome category every month, several months in advance. Which solution will meet these requirements?. Perform classification every month by using supervised learning of the 200 outcome categories based on claim contents. Perform reinforcement learning by using claim IDs and dates. Instruct the insurance agents who submit the claim records to estimate the expected number of claims in each outcome category every month. Perform forecasting by using claim IDs and dates to identify the expected number of claims in each outcome category every month. Most Voted. Perform classification by using supervised learning of the outcome categories for which partial information on claim contents is provided. Perform forecasting by using claim IDs and dates for all other outcome categories.

A retail company stores 100 GB of daily transactional data in Amazon S3 at periodic intervals. The company wants to identify the schema of the transactional data. The company also wants to perform transformations on the transactional data that is in Amazon S3. The company wants to use a machine learning (ML) approach to detect fraud in the transformed data. Which combination of solutions will meet these requirements with the LEAST operational overhead? (Choose three.). A. Use Amazon Athena to scan the data and identify the schema. Use AWS Glue crawlers to scan the data and identify the schema. Use Amazon Redshift to store procedures to perform data transformations. Use AWS Glue workflows and AWS Glue jobs to perform data transformations. Use Amazon Redshift ML to train a model to detect fraud. Use Amazon Fraud Detector to train a model to detect fraud.

A data scientist uses Amazon SageMaker Data Wrangler to define and perform transformations and feature engineering on historical data. The data scientist saves the transformations to SageMaker Feature Store.The historical data is periodically uploaded to an Amazon S3 bucket. The data scientist needs to transform the new historic data and add it to the online feature store. The data scientist needs to prepare the new historic data for training and inference by using native integrations. Which solution will meet these requirements with the LEAST development effort?. A. Use AWS Lambda to run a predefined SageMaker pipeline to perform the transformations on each new dataset that arrives in the S3 bucket. B. Run an AWS Step Functions step and a predefined SageMaker pipeline to perform the transformations on each new dataset that arrives in the S3 bucket. C. Use Apache Airflow to orchestrate a set of predefined transformations on each new dataset that arrives in the S3 bucket. D. Configure Amazon EventBridge to run a predefined SageMaker pipeline to perform the transformations when a new data is detected in the S3 bucket.

An insurance company developed a new experimental machine learning (ML) model to replace an existing model that is in production. The company must validate the quality of predictions from the new experimental model in a production environment before the company uses the new experimental model to serve general user requests. New one model can serve user requests at a time. The company must measure the performance of the new experimental model without affecting the current live traffic. Which solution will meet these requirements?. A/B testing. Canary release. Shadow deployment. Blue/green deployment.

A company deployed a machine learning (ML) model on the company website to predict real estate prices. Several months after deployment, an ML engineer notices that the accuracy of the model has gradually decreased. The ML engineer needs to improve the accuracy of the model. The engineer also needs to receive notifications for any future performance issues. Which solution will meet these requirements?. Perform incremental training to update the model. Activate Amazon SageMaker Model Monitor to detect model performance issues and to send notifications. Most Voted. Use Amazon SageMaker Model Governance. Configure Model Governance to automatically adjust model hyperparameters. Create a performance threshold alarm in Amazon CloudWatch to send notifications. Use Amazon SageMaker Debugger with appropriate thresholds. Configure Debugger to send Amazon CloudWatch alarms to alert the team. Retrain the model by using only data from the previous several months. Use only data from the previous several months to perform incremental training to update the model. Use Amazon SageMaker Model Monitor to detect model performance issues and to send notifications.

A university wants to develop a targeted recruitment strategy to increase new student enrollment. A data scientist gathers information about the academic performance history of students. The data scientist wants to use the data to build student profiles. The university will use the profiles to direct resources to recruit students who are likely to enroll in the university. Which combination of steps should the data scientist take to predict whether a particular student applicant is likely to enroll in the university? (Choose two.). Use Amazon SageMaker Ground Truth to sort the data into two groups named "enrolled" or "not enrolled.". Use a forecasting algorithm to run predictions. Use a regression algorithm to run predictions. Use a classification algorithm to run predictions. Use the built-in Amazon SageMaker k-means algorithm to cluster the data into two groups named "enrolled" or "not enrolled.".

A machine learning (ML) specialist is using the Amazon SageMaker DeepAR forecasting algorithm to train a model on CPU-based Amazon EC2 On-Demand instances. The model currently takes multiple hours to train. The ML specialist wants to decrease the training time of the model. Which approaches will meet this requirement? (Choose two.). Replace On-Demand Instances with Spot Instances. Configure model auto scaling dynamically to adjust the number of instances automatically. Replace CPU-based EC2 instances with GPU-based EC2 instances. Use multiple training instances. Use a pre-trained version of the model. Run incremental training.

chemical company has developed several machine learning (ML) solutions to identify chemical process abnormalities. The time series values of independent variables and the labels are available for the past 2 years and are sufficient to accurately model the problem. The regular operation label is marked as 0 The abnormal operation label is marked as 1. Process abnormalities have a significant negative effect on the company’s profits. The company must avoid these abnormalities. Which metrics will indicate an ML solution that will provide the GREATEST probability of detecting an abnormality?. Precision = 0.91 - Recall = 0.6. Precision = 0.61 - Recall = 0.98. Precision = 0.7 - Recall = 0.9. Precision = 0.98 - Recall = 0.8.

An online delivery company wants to choose the fastest courier for each delivery at the moment an order is placed. The company wants to implement this feature for existing users and new users of its application. Data scientists have trained separate models with XGBoost for this purpose, and the models are stored in Amazon S3. There is one model for each city where the company operates. Operation engineers are hosting these models in Amazon EC2 for responding to the web client requests, with one instance for each model, but the instances have only a 5% utilization in CPU and memory. The operation engineers want to avoid managing unnecessary resources. Which solution will enable the company to achieve its goal with the LEAST operational overhead?. Create an Amazon SageMaker notebook instance for pulling all the models from Amazon S3 using the boto3 library. Remove the existing instances and use the notebook to perform a SageMaker batch transform for performing inferences offline for all the possible users in all the cities. Store the results in different files in Amazon S3. Point the web client to the files. e URL and EndpointName parameter according to the city of each request. Prepare an Amazon SageMaker Docker container based on the open-source multi-model server. Remove the existing instances and create a multi-model endpoint in SageMaker instead, pointing to the S3 bucket containing all the models. Invoke the endpoint from the web client at runtime, specifying the TargetModel parameter according to the city of each request. Keep only a single EC2 instance for hosting all the models. Install a model server in the instance and load each model by pulling it from Amazon S3. Integrate the instance with the web client using Amazon API Gateway for responding to the requests in real time, specifying the target resource according to the city of each request. Prepare a Docker container based on the prebuilt images in Amazon SageMaker. Replace the existing instances with separate SageMaker endpoints, one for each city where the company operates. Invoke the endpoints from the web client, specifying the URL and EndpointName parameter according to the city of each request.

A company builds computer-vision models that use deep learning for the autonomous vehicle industry. A machine learning (ML) specialist uses an Amazon EC2 instance that has a CPU:GPU ratio of 12:1 to train the models. The ML specialist examines the instance metric logs and notices that the GPU is idle half of the time. The ML specialist must reduce training costs without increasing the duration of the training jobs. Which solution will meet these requirements?. Switch to an instance type that has only CPUs. Use a heterogeneous cluster that has two different instances groups. Use memory-optimized EC2 Spot Instances for the training jobs. Switch to an instance type that has a CPU:GPU ratio of 6:1.

A company wants to forecast the daily price of newly launched products based on 3 years of data for older product prices, sales, and rebates. The time-series data has irregular timestamps and is missing some values. Data scientist must build a dataset to replace the missing values. The data scientist needs a solution that resamples the data daily and exports the data for further modeling. Which solution will meet these requirements with the LEAST implementation effort?. Use Amazon EMR Serverless with PySpark. Use AWS Glue DataBrew. Use Amazon SageMaker Studio Data Wrangler. Use Amazon SageMaker Studio Notebook with Pandas.

A data scientist is building a forecasting model for a retail company by using the most recent 5 years of sales records that are stored in a data warehouse. The dataset contains sales records for each of the company’s stores across five commercial regions. The data scientist creates a working dataset with StoreID. Region. Date, and Sales Amount as columns. The data scientist wants to analyze yearly average sales for each region. The scientist also wants to compare how each region performed compared to average sales across all commercial regions. Which visualization will help the data scientist better understand the data trend?. Create an aggregated dataset by using the Pandas GroupBy function to get average sales for each year for each store. Create a bar plot, faceted by year, of average sales for each store. Add an extra bar in each facet to represent average sales. Create an aggregated dataset by using the Pandas GroupBy function to get average sales for each year for each store. Create a bar plot, colored by region and faceted by year, of average sales for each store. Add a horizontal line in each facet to represent average sales. Create an aggregated dataset by using the Pandas GroupBy function to get average sales for each year for each region. Create a bar plot of average sales for each region. Add an extra bar in each facet to represent average sales. Create an aggregated dataset by using the Pandas GroupBy function to get average sales for each year for each region. Create a bar plot, faceted by year, of average sales for each region. Add a horizontal line in each facet to represent average sales.

A company uses sensors on devices such as motor engines and factory machines to measure parameters, temperature and pressure. The company wants to use the sensor data to predict equipment malfunctions and reduce services outages. Machine learning (ML) specialist needs to gather the sensors data to train a model to predict device malfunctions. The ML specialist must ensure that the data does not contain outliers before training the model. How can the ML specialist meet these requirements with the LEAST operational overhead?. Load the data into an Amazon SageMaker Studio notebook. Calculate the first and third quartile. Use a SageMaker Data Wrangler data flow to remove only values that are outside of those quartiles. Use an Amazon SageMaker Data Wrangler bias report to find outliers in the dataset. Use a Data Wrangler data flow to remove outliers based on the bias report. Use an Amazon SageMaker Data Wrangler anomaly detection visualization to find outliers in the dataset. Add a transformation to a Data Wrangler data flow to remove outliers. Use Amazon Lookout for Equipment to find and remove outliers from the dataset.

A data scientist obtains a tabular dataset that contains 150 correlated features with different ranges to build a regression model. The data scientist needs to achieve more efficient model training by implementing a solution that minimizes impact on the model’s performance. The data scientist decides to perform a principal component analysis (PCA) preprocessing step to reduce the number of features to a smaller set of independent features before the data scientist uses the new features in the regression model. Which preprocessing step will meet these requirements?. Use the Amazon SageMaker built-in algorithm for PCA on the dataset to transform the data. Load the data into Amazon SageMaker Data Wrangler. Scale the data with a Min Max Scaler transformation step. Use the SageMaker built-in algorithm for PCA on the scaled dataset to transform the data. Reduce the dimensionality of the dataset by removing the features that have the highest correlation. Load the data into Amazon SageMaker Data Wrangler. Perform a Standard Scaler transformation step to scale the data. Use the SageMaker built-in algorithm for PCA on the scaled dataset to transform the data. Reduce the dimensionality of the dataset by removing the features that have the lowest correlation. Load the data into Amazon SageMaker Data Wrangler. Perform a Min Max Scaler transformation step to scale the data. Use the SageMaker built-in algorithm for PCA on the scaled dataset to transform the data.

An online retailer collects the following data on customer orders: demographics, behaviors, location, shipment progress, and delivery time. A data scientist joins all the collected datasets. The result is a single dataset that includes 980 variables. The data scientist must develop a machine learning (ML) model to identify groups of customers who are likely to respond to a marketing campaign. Which combination of algorithms should the data scientist use to meet this requirement? (Choose two.). Latent Dirichlet Allocation (LDA). K-means. Semantic segmentation. Principal component analysis (PCA). Factorization machines (FM).

A machine learning engineer is building a bird classification model. The engineer randomly separates a dataset into a training dataset and a validation dataset. During the training phase, the model achieves very high accuracy. However, the model did not generalize well during validation of the validation dataset. The engineer realizes that the original dataset was imbalanced. What should the engineer do to improve the validation accuracy of the model?. Perform stratified sampling on the original dataset. Acquire additional data about the majority classes in the original dataset. Use a smaller, randomly sampled version of the training dataset. Perform systematic sampling on the original dataset.

A data engineer wants to perform exploratory data analysis (EDA) on a petabyte of data. The data engineer does not want to manage compute resources and wants to pay only for queries that are run. The data engineer must write the analysis by using Python from a Jupyter notebook. Which solution will meet these requirements?. Use Apache Spark from within Amazon Athena. Use Apache Spark from within Amazon SageMaker. Use Apache Spark from within an Amazon EMR cluster. Use Apache Spark through an integration with Amazon Redshift.

A data scientist receives a new dataset in .csv format and stores the dataset in Amazon S3. The data scientist will use the dataset to train a machine learning (ML) model. The data scientist first needs to identify any potential data quality issues in the dataset. The data scientist must identify values that are missing or values that are not valid. The data scientist must also identify the number of outliers in the dataset. Which solution will meet these requirements with the LEAST operational effort?. Create an AWS Glue job to transform the data from .csv format to Apache Parquet format. Use an AWS Glue crawler and Amazon Athena with appropriate SQL queries to retrieve the required information. Leave the dataset in .csv format. Use an AWS Glue crawler and Amazon Athena with appropriate SQL queries to retrieve the required information. Create an AWS Glue job to transform the data from .csv format to Apache Parquet format. Import the data into Amazon SageMaker Data Wrangler. Use the Data Quality and Insights Report to retrieve the required information. Leave the dataset in .csv format. Import the data into Amazon SageMaker Data Wrangler. Use the Data Quality and Insights Report to retrieve the required information.

An ecommerce company has developed a XGBoost model in Amazon SageMaker to predict whether a customer will return a purchased item. The dataset is imbalanced. Only 5% of customers return items. A data scientist must find the hyperparameters to capture as many instances of returned items as possible. The company has a small budget for compute. How should the data scientist meet these requirements MOST cost-effectively?. A. Tune all possible hyperparameters by using automatic model tuning (AMT). Optimize on {"HyperParameterTuningJobObjective": {"MetricName": "validation:accuracy", "Type": "Maximize"}}. Tune the csv_weight hyperparameter and the scale_pos_weight hyperparameter by using automatic model tuning (AMT). Optimize on {"HyperParameterTuningJobObjective": {"MetricName": "validation'll", "Type": "Maximize"}}. Tune all possible hyperparameters by using automatic model tuning (AMT). Optimize on {"HyperParameterTuningJobObjective": {"MetricName": "validation:f1", "Type": "Maximize"}}. Tune the csv_weight hyperparameter and the scale_pos_weight hyperparameter by using automatic model tuning (AMT). Optimize on {"HyperParameterTuningJobObjective": {"MetricName": "validation:f1", "Type": "Minimize"}}.

A data scientist is trying to improve the accuracy of a neural network classification model. The data scientist wants to run a large hyperparameter tuning job in Amazon SageMaker. However, previous smaller tuning jobs on the same model often ran for several weeks. The ML specialist wants to reduce the computation time required to run the tuning job. Which actions will MOST reduce the computation time for the hyperparameter tuning job? (Choose two.). Use the Hyperband tuning strategy. Increase the number of hyperparameters. Set a lower value for the MaxNumberOfTrainingJobs parameter. Use the grid search tuning strategy. Set a lower value for the MaxParallelTrainingJobs parameter.

A machine learning (ML) specialist needs to solve a binary classification problem for a marketing dataset. The ML specialist must maximize the Area Under the ROC Curve (AUC) of the algorithm by training an XGBoost algorithm. The ML specialist must find values for the eta, alpha, min_child_weight, and max_depth hyperparameters that will generate the most accurate model. Which approach will meet these requirements with the LEAST operational overhead?. Use a bootstrap script to install scikit-learn on an Amazon EMR cluster. Deploy the EMR cluster. Apply k-fold cross-validation methods to the algorithm. Deploy Amazon SageMaker prebuilt Docker images that have scikit-learn installed. Apply k-fold cross-validation methods to the algorithm. Use Amazon SageMaker automatic model tuning (AMT). Specify a range of values for each hyperparameter. Subscribe to an AUC algorithm that is on AWS Marketplace. Specify a range of values for each hyperparameter.

A machine learning (ML) developer for an online retailer recently uploaded a sales dataset into Amazon SageMaker Studio. The ML developer wants to obtain importance scores for each feature of the dataset. The ML developer will use the importance scores to feature engineer the dataset. Which solution will meet this requirement with the LEAST development effort?. Use SageMaker Data Wrangler to perform a Gini importance score analysis. Use a SageMaker notebook instance to perform principal component analysis (PCA). Use a SageMaker notebook instance to perform a singular value decomposition analysis. Use the multicollinearity feature to perform a lasso feature selection to perform an importance scores analysis.

A company is setting up a mechanism for data scientists and engineers from different departments to access an Amazon SageMaker Studio domain. Each department has a unique SageMaker Studio domain. The company wants to build a central proxy application that data scientists and engineers can log in to by using their corporate credentials. The proxy application will authenticate users by using the company's existing Identity provider (IdP). The application will then route users to the appropriate SageMaker Studio domain. The company plans to maintain a table in Amazon DynamoDB that contains SageMaker domains for each department. How should the company meet these requirements?. Use the SageMaker CreatePresignedDomainUrl API to generate a presigned URL for each domain according to the DynamoDB table. Pass the presigned URL to the proxy application. Use the SageMaker CreateHumanTaskUi API to generate a UI URL. Pass the URL to the proxy application. Use the Amazon SageMaker ListHumanTaskUis API to list all UI URLs. Pass the appropriate URL to the DynamoDB table so that the proxy application can use the URL. Use the SageMaker CreatePresignedNotebooklnstanceUrl API to generate a presigned URL. Pass the presigned URL to the proxy application.

An insurance company is creating an application to automate car insurance claims. A machine learning (ML) specialist used an Amazon SageMaker Object Detection - TensorFlow built-in algorithm to train a model to detect scratches and dents in images of cars. After the model was trained, the ML specialist noticed that the model performed better on the training dataset than on the testing dataset. Which approach should the ML specialist use to improve the performance of the model on the testing data?. Increase the value of the momentum hyperparameter. Reduce the value of the dropout_rate hyperparameter. Reduce the value of the learning_rate hyperparameter. Increase the value of the L2 hyperparameter.

A developer at a retail company is creating a daily demand forecasting model. The company stores the historical hourly demand data in an Amazon S3 bucket. However, the historical data does not include demand data for some hours. The developer wants to verify that an autoregressive integrated moving average (ARIMA) approach will be a suitable model for the use case. How should the developer verify the suitability of an ARIMA approach. Use Amazon SageMaker Data Wrangler. Import the data from Amazon S3. Impute hourly missing data. Perform a Seasonal Trend decomposition. Use Amazon SageMaker Autopilot. Create a new experiment that specifies the S3 data location. Choose ARIMA as the machine learning (ML) problem. Check the model performance. Use Amazon SageMaker Data Wrangler. Import the data from Amazon S3. Resample data by using the aggregate daily total. Perform a Seasonal Trend decomposition. Use Amazon SageMaker Autopilot. Create a new experiment that specifies the S3 data location. Impute missing hourly values. Choose ARIMA as the machine learning (ML) problem. Check the model performance.

A company decides to use Amazon SageMaker to develop machine learning (ML) models. The company will host SageMaker notebook instances in a VPC. The company stores training data in an Amazon S3 bucket. Company security policy states that SageMaker notebook instances must not have internet connectivity. Which solution will meet the company’s security requirements?. Connect the SageMaker notebook instances that are in the VPC by using AWS Site-to-Site VPN to encrypt all internet-bound traffic. Configure VPC flow logs. Monitor all network traffic to detect and prevent any malicious activity. Configure the VPC that contains the SageMaker notebook instances to use VPC interface endpoints to establish connections for training and hosting. Modify any existing security groups that are associated with the VPC interface endpoint to allow only outbound connections for training and hosting. Create an IAM policy that prevents access the internet. Apply the IAM policy to an IAM role. Assign the IAM role to the SageMaker notebook instances in addition to any IAM roles that are already assigned to the instances. Create VPC security groups to prevent all incoming and outgoing traffic. Assign the security groups to the SageMaker notebook instances.

A machine learning (ML) engineer uses Bayesian optimization for a hyperpara meter tuning job in Amazon SageMaker. The ML engineer uses precision as the objective metric. The ML engineer wants to use recall as the objective metric. The ML engineer also wants to expand the hyperparameter range for a new hyperparameter tuning job. The new hyperparameter range will include the range of the previously performed tuning job. Which approach will run the new hyperparameter tuning job in the LEAST amount of time?. Use a warm start hyperparameter tuning job. Use a checkpointing hyperparameter tuning job. Use the same random seed for the hyperparameter tuning job. Use multiple jobs in parallel for the hyperparameter tuning job.

A news company is developing an article search tool for its editors. The search tool should look for the articles that are most relevant and representative for particular words that are queried among a corpus of historical news documents. The editors test the first version of the tool and report that the tool seems to look for word matches in general. The editors have to spend additional time to filter the results to look for the articles where the queried words are most important. A group of data scientists must redesign the tool so that it isolates the most frequently used words in a document. The tool also must capture the relevance and importance of words for each document in the corpus. Which solution meets these requirements?. Extract the topics from each article by using Latent Dirichlet Allocation (LDA) topic modeling. Create a topic table by assigning the sum of the topic counts as a score for each word in the articles. Configure the tool to retrieve the articles where this topic count score is higher for the queried words. Build a term frequency for each word in the articles that is weighted with the article's length. Build an inverse document frequency for each word that is weighted with all articles in the corpus. Define a final highlight score as the product of both of these frequencies. Configure the tool to retrieve the articles where this highlight score is higher for the queried words. Most Voted. Download a pretrained word-embedding lookup table. Create a titles-embedding table by averaging the title's word embedding for each article in the corpus. Define a highlight score for each word as inversely proportional to the distance between its embedding and the title embedding. Configure the tool to retrieve the articles where this highlight score is higher for the queried words. Build a term frequency score table for each word in each article of the corpus. Assign a score of zero to all stop words. For any other words, assign a score as the word’s frequency in the article. Configure the tool to retrieve the articles where this frequency score is higher for the queried words.

A growing company has a business-critical key performance indicator (KPI) for the uptime of a machine learning (ML) recommendation system. The company is using Amazon SageMaker hosting services to develop a recommendation model in a single Availability Zone within an AWS Region. A machine learning (ML) specialist must develop a solution to achieve high availability. The solution must have a recovery time objective (RTO) of 5 minutes. Which solution will meet these requirements with the LEAST effort?. Deploy multiple instances for each endpoint in a VPC that spans at least two Regions. Use the SageMaker auto scaling feature for the hosted recommendation models. Deploy multiple instances for each production endpoint in a VPC that spans least two subnets that are in a second Availability Zone. Frequently generate backups of the production recommendation model. Deploy the backups in a second Region.

A global company receives and processes hundreds of documents daily. The documents are in printed .pdf format or .jpg format. A machine learning (ML) specialist wants to build an automated document processing workflow to extract text from specific fields from the documents and to classify the documents. The ML specialist wants a solution that requires low maintenance. Which solution will meet these requirements with the LEAST operational effort?. Use a PaddleOCR model in Amazon SageMaker to detect and extract the required text and fields. Use a SageMaker text classification model to classify the document. Use a PaddleOCR model in Amazon SageMaker to detect and extract the required text and fields. Use Amazon Comprehend to classify the document. Use Amazon Textract to detect and extract the required text and fields. Use Amazon Rekognition to classify the document. Use Amazon Textract to detect and extract the required text and fields. Use Amazon Comprehend to classify the document.

A company wants to detect credit card fraud. The company has observed that an average of 2% of credit card transactions are fraudulent. A data scientist trains a classifier on a year's worth of credit card transaction data. The classifier needs to identify the fraudulent transactions. The company wants to accurately capture as many fraudulent transactions as possible. Which metrics should the data scientist use to optimize the classifier? (Choose two.). Specificity. False positive rate. Accuracy. F1 score Most Voted. True positive rate Most Voted.

A data scientist is designing a repository that will contain many images of vehicles. The repository must scale automatically in size to store new images every day. The repository must support versioning of the images. The data scientist must implement a solution that maintains multiple immediately accessible copies of the data in different AWS Regions. Which solution will meet these requirements?. Amazon S3 with S3 Cross-Region Replication (CRR). Amazon Elastic Block Store (Amazon EBS) with snapshots that are shared in a secondary Region. Amazon Elastic File System (Amazon EFS) Standard storage that is configured with Regional availability. AWS Storage Gateway Volume Gateway.

An ecommerce company wants to update a production real-time machine learning (ML) recommendation engine API that uses Amazon SageMaker. The company wants to release a new model but does not want to make changes to applications that rely on the API. The company also wants to evaluate the performance of the new model in production traffic before the company fully rolls out the new model to all users. Which solution will meet these requirements with the LEAST operational overhead?. Create a new SageMaker endpoint for the new model. Configure an Application Load Balancer (ALB) to distribute traffic between the old model and the new model. Modify the existing endpoint to use SageMaker production variants to distribute traffic between the old model and the new model. Modify the existing endpoint to use SageMaker batch transform to distribute traffic between the old model and the new model. Create a new SageMaker endpoint for the new model. Configure a Network Load Balancer (NLB) to distribute traffic between the old model and the new model.

A machine learning (ML) specialist at a manufacturing company uses Amazon SageMaker DeepAR to forecast input materials and energy requirements for the company. Most of the data in the training dataset is missing values for the target variable. The company stores the training dataset as JSON files. The ML specialist develop a solution by using Amazon SageMaker DeepAR to account for the missing values in the training dataset. Which approach will meet these requirements with the LEAST development effort?. Impute the missing values by using the linear regression method. Use the entire dataset and the imputed values to train the DeepAR model. Replace the missing values with not a number (NaN). Use the entire dataset and the encoded missing values to train the DeepAR model. Impute the missing values by using a forward fill. Use the entire dataset and the imputed values to train the DeepAR model. Impute the missing values by using the mean value. Use the entire dataset and the imputed values to train the DeepAR model.

A law firm handles thousands of contracts every day. Every contract must be signed. Currently, a lawyer manually checks all contracts for signatures. The law firm is developing a machine learning (ML) solution to automate signature detection for each contract. The ML solution must also provide a confidence score for each contract page. Which Amazon Textract API action can the law firm use to generate a confidence score for each page of each contract?. Use the AnalyzeDocument API action. Set the FeatureTypes parameter to SIGNATURES. Return the confidence scores for each page. Use the Prediction API call on the documents. Return the signatures and confidence scores for each page. Use the StartDocumentAnalysis API action to detect the signatures. Return the confidence scores for each page. Use the GetDocumentAnalysis API action to detect the signatures. Return the confidence scores for each page.

A company that operates oil platforms uses drones to photograph locations on oil platforms that are difficult for humans to access to search for corrosion. Experienced engineers review the photos to determine the severity of corrosion. There can be several corroded areas in a single photo. The engineers determine whether the identified corrosion needs to be fixed immediately, scheduled for future maintenance, or requires no action. The corrosion appears in an average of 0.1% of all photos. A data science team needs to create a solution that automates the process of reviewing the photos and classifying the need for maintenance. Which combination of steps will meet these requirements? (Choose three.). Use an object detection algorithm to train a model to identify corrosion areas of a photo. Most Voted. Use Amazon Rekognition with label detection on the photos. Use a k-means clustering algorithm to train a model to classify the severity of corrosion in a photo. Use an XGBoost algorithm to train a model to classify the severity of corrosion in a photo. Most Voted. Perform image augmentation on photos that contain corrosion. Most Voted. Perform image augmentation on photos that do not contain corrosion.

A company maintains a 2 TB dataset that contains information about customer behaviors. The company stores the dataset in Amazon S3. The company stores a trained model container in Amazon Elastic Container Registry (Amazon ECR). A machine learning (ML) specialist needs to score a batch model for the dataset to predict customer behavior. The ML specialist must select a scalable approach to score the model. Which solution will meet these requirements MOST cost-effectively?. Score the model by using AWS Batch managed Amazon EC2 Reserved Instances. Create an Amazon EC2 instance store volume and mount it to the Reserved Instances. Score the model by using AWS Batch managed Amazon EC2 Spot Instances. Create an Amazon FSx for Lustre volume and mount it to the Spot Instances. Score the model by using an Amazon SageMaker notebook on Amazon EC2 Reserved Instances. Create an Amazon EBS volume and mount it to the Reserved Instances. Score the model by using Amazon SageMaker notebook on Amazon EC2 Spot Instances. Create an Amazon Elastic File System (Amazon EFS) file system and mount it to the Spot Instances.

A data scientist is implementing a deep learning neural network model for an object detection task on images. The data scientist wants to experiment with a large number of parallel hyperparameter tuning jobs to find hyperparameters that optimize compute time. The data scientist must ensure that jobs that underperform are stopped. The data scientist must allocate computational resources to well-performing hyperparameter configurations. The data scientist is using the hyperparameter tuning job to tune the stochastic gradient descent (SGD) learning rate, momentum, epoch, and mini-batch size. Which technique will meet these requirements with LEAST computational time?. Grid search. Random search. Bayesian optimization. Hyperband Most Voted.

An agriculture company wants to improve crop yield forecasting for the upcoming season by using crop yields from the last three seasons. The company wants to compare the performance of its new scikit-learn model to the benchmark. A data scientist needs to package the code into a container that computes both the new model forecast and the benchmark. The data scientist wants AWS to be responsible for the operational maintenance of the container. Which solution will meet these requirements?. Package the code as the training script for an Amazon SageMaker scikit-learn container. Package the code into a custom-built container. Push the container to Amazon Elastic Container Registry (Amazon ECR). Package the code into a custom-built container. Push the container to AWS Fargate. Package the code by extending an Amazon SageMaker scikit-learn container.

A cybersecurity company is collecting on-premises server logs, mobile app logs, and IoT sensor data. The company backs up the ingested data in an Amazon S3 bucket and sends the ingested data to Amazon OpenSearch Service for further analysis. Currently, the company has a custom ingestion pipeline that is running on Amazon EC2 instances. The company needs to implement a new serverless ingestion pipeline that can automatically scale to handle sudden changes in the data flow. Which solution will meet these requirements MOST cost-effectively?. Create two Amazon Data Firehose delivery streams to send data to the S3 bucket and OpenSearch Service. Configure the data sources to send data to the delivery streams. Create one Amazon Kinesis data stream. Create two Amazon Data Firehose delivery streams to send data to the S3 bucket and OpenSearch Service. Connect the delivery streams to the data stream. Configure the data sources to send data to the data stream. Create one Amazon Data Firehose delivery stream to send data to OpenSearch Service. Configure the delivery stream to back up the raw data to the S3 bucket. Configure the data sources to send data to the delivery stream. Create one Amazon Kinesis data stream. Create one Amazon Data Firehose delivery stream to send data to OpenSearch Service. Configure the delivery stream to back up the data to the S3 bucket. Connect the delivery stream to the data stream. Configure the data sources to send data to the data stream.

A bank has collected customer data for 10 years in CSV format. The bank stores the data in an on-premises server. A data science team wants to use Amazon SageMaker to build and train a machine learning (ML) model to predict churn probability. The team will use the historical data. The data scientists want to perform data transformations quickly and to generate data insights before the team builds a model for production. Which solution will meet these requirements with the LEAST development effort?. Upload the data into the SageMaker Data Wrangler console directly. Perform data transformations and generate insights within Data Wrangler. Upload the data into an Amazon S3 bucket. Allow SageMaker to access the data that is in the bucket. Import the data from the S3 bucket into SageMaker Data Wrangler. Perform data transformations and generate insights within Data Wrangler. Upload the data into the SageMaker Data Wrangler console directly. Allow SageMaker and Amazon QuickSight to access the data that is in an Amazon S3 bucket. Perform data transformations in Data Wrangler and save the transformed data into a second S3 bucket. Use QuickSight to generate data insights. Upload the data into an Amazon S3 bucket. Allow SageMaker to access the data that is in the bucket. Import the data from the bucket into SageMaker Data Wrangler. Perform data transformations in Data Wrangler. Save the data into a second S3 bucket. Use a SageMaker Studio notebook to generate data insights.

A media company wants to deploy a machine learning (ML) model that uses Amazon SageMaker to recommend new articles to the company’s readers. The company's readers are primarily located in a single city. The company notices that the heaviest reader traffic predictably occurs early in the morning, after lunch, and again after work hours. There is very little traffic at other times of day. The media company needs to minimize the time required to deliver recommendations to its readers. The expected amount of data that the API call will return for inference is less than 4 MB. Which solution will meet these requirements in the MOST cost-effective way?. Real-time inference with auto scaling. Serverless inference with provisioned concurrency. Asynchronous inference. A batch transform task.

A machine learning (ML) engineer is using Amazon SageMaker automatic model tuning (AMT) to optimize a model's hyperparameters. The ML engineer notices that the tuning jobs take a long time to run. The tuning jobs continue even when the jobs are not significantly improving against the objective metric. The ML engineer needs the training jobs to optimize the hyperparameters more quickly. How should the ML engineer configure the SageMaker AMT data types to meet these requirements?. Set Strategy to the Bayesian value. Set RetryStrategy to a value of 1. Set ParameterRanges to the narrow range Inferred from previous hyperparameter jobs. Set TrainingJobEarlyStoppingType to the AUTO value.

A global bank requires a solution to predict whether customers will leave the bank and choose another bank. The bank is using a dataset to train a model to predict customer loss. The training dataset has 1,000 rows. The training dataset includes 100 instances of customers who left the bank. A machine learning (ML) specialist is using Amazon SageMaker Data Wrangler to train a churn prediction model by using a SageMaker training job. After training, the ML specialist notices that the model returns only false results. The ML specialist must correct the model so that it returns more accurate predictions. Which solution will meet these requirements?. Apply anomaly detection to remove outliers from the training dataset before training. Apply Synthetic Minority Oversampling Technique (SMOTE) to the training dataset before training. Apply normalization to the features of the training dataset before training. Apply undersampling to the training dataset before training.

banking company provides financial products to customers around the world. A machine learning (ML) specialist collected transaction data from internal customers. The ML specialist split the dataset into training, testing, and validation datasets. The ML specialist analyzed the training dataset by using Amazon SageMaker Clarify. The analysis found that the training dataset contained fewer examples of customers in the 40 to 55 year-old age group compared to the other age groups. Which type of pretraining bias did the ML specialist observe in the training dataset?. Difference in proportions of labels (DPL). Class imbalance (CI). Conditional demographic disparity (CDD). Kolmogorov-Smirnov (KS).

A tourism company uses a machine learning (ML) model to make recommendations to customers. The company uses an Amazon SageMaker environment and set hyperparameter tuning completion criteria to MaxNumberOfTrainingJobs. An ML specialist wants to change the hyperparameter tuning completion criteria. The ML specialist wants to stop tuning immediately after an internal algorithm determines that tuning job is unlikely to improve more than 1% over the objective metric from the best training job. Which completion criteria will meet this requirement?. MaxRuntimeInSeconds. TargetObjectiveMetricValue. CompleteOnConvergence. MaxNumberOfTrainingJobsNotImproving.

A car company has dealership locations in multiple cities. The company uses a machine learning (ML) recommendation system to market cars to its customers. An ML engineer trained the ML recommendation model on a dataset that includes multiple attributes about each car. The dataset includes attributes such as car brand, car type, fuel efficiency, and price. The ML engineer uses Amazon SageMaker Data Wrangler to analyze and visualize data. The ML engineer needs to identify the distribution of car prices for a specific type of car. Which type of visualization should the ML engineer use to meet these requirements?. Use the SageMaker Data Wrangler scatter plot visualization to inspect the relationship between the car price and type of car. Use the SageMaker Data Wrangler scatter plot visualization to inspect the relationship between the car price and type of car. Use the SageMaker Data Wrangler anomaly detection visualization to Identify outliers for the specific features. Use the SageMaker Data Wrangler histogram visualization to inspect the range of values for the specific feature. Most Voted.

A media company is building a computer vision model to analyze images that are on social media. The model consists of CNNs that the company trained by using images that the company stores in Amazon S3. The company used an Amazon SageMaker training job in File mode with a single Amazon EC2 On-Demand Instance. Every day, the company updates the model by using about 10,000 images that the company has collected in the last 24 hours. The company configures training with only one epoch. The company wants to speed up training and lower costs without the need to make any code changes. Which solution will meet these requirements?. Instead of File mode, configure the SageMaker training job to use Pipe mode. Ingest the data from a pipe. Instead of File mode, configure the SageMaker training job to use FastFile mode with no other changes. Instead of On-Demand Instances, configure the SageMaker training job to use Spot Instances. Make no other changes,. Instead of On-Demand Instances, configure the SageMaker training job to use Spot Instances, implement model checkpoints.

A telecommunications company has deployed a machine learning model using Amazon SageMaker. The model identifies customers who are likely to cancel their contract when calling customer service. These customers are then directed to a specialist service team. The model has been trained on historical data from multiple years relating to customer contracts and customer service interactions in a single geographic region. The company is planning to launch a new global product that will use this model. Management is concerned that the model might incorrectly direct a large number of calls from customers in regions without historical data to the specialist service team. Which approach would MOST effectively address this issue?. Enable Amazon SageMaker Model Monitor data capture on the model endpoint. Create a monitoring baseline on the training dataset. Schedule monitoring jobs. Use Amazon CloudWatch to alert the data scientists when the numerical distance of regional customer data fails the baseline drift check. Reevaluate the training set with the larger data source and retrain the model. Enable Amazon SageMaker Debugger on the model endpoint. Create a custom rule to measure the variance from the baseline training dataset. Use Amazon CloudWatch to alert the data scientists when the rule is invoked. Reevaluate the training set with the larger data source and retrain the model. Capture all customer calls routed to the specialist service team in Amazon S3. Schedule a monitoring job to capture all the true positives and true negatives, correlate them to the training dataset, and calculate the accuracy. Use Amazon CloudWatch to alert the data scientists when the accuracy decreases. Reevaluate the training set with the additional data from the specialist service team and retrain the model. Enable Amazon CloudWatch on the model endpoint. Capture metrics using Amazon CloudWatch Logs and send them to Amazon S3. Analyze the monitored results against the training data baseline. When the variance from the baseline exceeds the regional customer variance, reevaluate the training set and retrain the model.

A machine learning (ML) engineer is creating a binary classification model. The ML engineer will use the model in a highly sensitive environment. There is no cost associated with missing a positive label. However, the cost of making a false positive inference is extremely high. What is the most important metric to optimize the model for in this scenario?. Accuracy. Precision. Recall. F1.

An ecommerce company discovers that the search tool for the company's website is not presenting the top search results to customers. The company needs to resolve the issue so the search tool will present results that customers are most likely to want to purchase. Which solution will meet this requirement with the LEAST operational effort?. Use the Amazon SageMaker BlazingText algorithm to add context to search results through query expansion. Use the Amazon SageMaker XGBoost algorithm to improve candidate ranking. Use Amazon CloudSearch and sort results by the search relevance score. Use Amazon CloudSearch and sort results by the geographic location.

A machine learning (ML) specialist collected daily product usage data for a group of customers. The ML specialist appended customer metadata such as age and gender from an external data source. The ML specialist wants to understand product usage patterns for each day of the week for customers in specific age groups. The ML specialist creates two categorical features named dayofweek and binned_age, respectively. Which approach should the ML specialist use discover the relationship between the two new categorical features?. Create a scatterplot for day_of_week and binned_age. Create crosstabs for day_of_week and binned_age. Create word clouds for day_of_week and binned_age. Create a boxplot for day_of_week and binned_age.

A company needs to develop a model that uses a machine learning (ML) model for risk analysis. An ML engineer needs to evaluate the contribution each feature of a training dataset makes to the prediction of the target variable before the ML engineer selects features. How should the ML engineer predict the contribution of each feature?. Use the Amazon SageMaker Data Wrangler multicollinearity measurement features and the principal component analysis (PCA) algorithm to calculate the variance of the dataset along multiple directions in the feature space. Use an Amazon SageMaker Data Wrangler quick model visualization to find feature importance scores that are between 0.5 and 1. Use the Amazon SageMaker Data Wrangler bias report to identify potential biases in the data related to feature engineering. Use an Amazon SageMaker Data Wrangler data flow to create and modify a data preparation pipeline. Manually add the feature scores.

A company is building a predictive maintenance system using real-time data from devices on remote sites. There is no AWS Direct Connect connection or VPN connection between the sites and the company's VPC. The data needs to be ingested in real time from the devices into Amazon S3. Transformation is needed to convert the raw data into clean .csv data to be fed into the machine learning (ML) model. The transformation needs to happen during the ingestion process. When transformation fails, the records need to be stored in a specific location in Amazon S3 for human review. The raw data before transformation also needs to be stored in Amazon S3. How should an ML specialist architect the solution to meet these requirements with the LEAST effort?. Use Amazon Data Firehose with Amazon S3 as the destination. Configure Firehose to invoke an AWS Lambda function for data transformation. Enable source record backup on Firehose. Use Amazon Managed Streaming for Apache Kafka. Set up workers in Amazon Elastic Container Service (Amazon ECS) to move data from Kafka brokers to Amazon S3 while transforming it. Configure workers to store raw and unsuccessfully transformed data in different S3 buckets. Use Amazon Data Firehose with Amazon S3 as the destination. Configure Firehose to invoke an Apache Spark job in AWS Glue for data transformation. Enable source record backup and configure the error prefix. Use Amazon Kinesis Data Streams in front of Amazon Data Firehose. Use Kinesis Data Streams with AWS Lambda to store raw data in Amazon S3. Configure Firehose to invoke a Lambda function for data transformation with Amazon S3 as the destination.

A company wants to use machine learning (ML) to improve its customer churn prediction model. The company stores data in an Amazon Redshift data warehouse. A data science team wants to use Amazon Redshift machine learning (Amazon Redshift ML) to build a model and run predictions for new data directly within the data warehouse. Which combination of steps should the company take to use Amazon Redshift ML to meet these requirements? (Choose three.). Define the feature variables and target variable for the churn prediction model. Use the SOL EXPLAIN_MODEL function to run predictions. Write a CREATE MODEL SQL statement to create a model. Use Amazon Redshift Spectrum to train the model. Manually export the training data to Amazon S3. Use the SQL prediction function to run predictions.

A company’s machine learning (ML) team needs to build a system that can detect whether people in a collection of images are wearing the company’s logo. The company has a set of labeled training data. Which algorithm should the ML team use to meet this requirement?. Principal component analysis (PCA). Recurrent neural network (RNN). К-nearest neighbors (k-NN). Convolutional neural network (CNN).

A data scientist uses Amazon SageMaker Data Wrangler to obtain a feature summary from a dataset that the data scientist imported from Amazon S3. The data scientist notices that the prediction power for a dataset feature has a score of 1. What is the cause of the score?. Target leakage occurred in the imported dataset. The data scientist did not fine-tune the training and validation split. The SageMaker Data Wrangler algorithm that the data scientist used did not find an optimal model fit for each feature to calculate the prediction power. The data scientist did not process the features enough to accurately calculate prediction power.

A data scientist is conducting exploratory data analysis (EDA) on a dataset that contains information about product suppliers. The dataset records the country where each product supplier is located as a two-letter text code. For example, the code for New Zealand is "NZ." The data scientist needs to transform the country codes for model training. The data scientist must choose the solution that will result in the smallest increase in dimensionality. The solution must not result in any information loss. Which solution will meet these requirements?. Add a new column of data that includes the full country name. Encode the country codes into numeric variables by using similarity encoding. Map the country codes to continent names. Encode the country codes into numeric variables by using one-hot encoding.

A data scientist is building a new model for an ecommerce company. The model will predict how many minutes it will take to deliver a package. During model training, the data scientist needs to evaluate model performance. Which metrics should the data scientist use to meet this requirement? (Choose two.). InferenceLatency. Mean squared error (MSE). Root mean squared error (RMSE). Precision. Accuracy.

A machine learning (ML) specialist is developing a model for a company. The model will classify and predict sequences of objects that are displayed in a video. The ML specialist decides to use a hybrid architecture that consists of a convolutional neural network (CNN) followed by a classifier three-layer recurrent neural network (RNN). The company developed a similar model previously but trained the model to classify a different set of objects. The ML specialist wants to save time by using the previously trained model and adapting the model for the current use case and set of objects. Which combination of steps will accomplish this goal with the LEAST amount of effort? (Choose two.). Reinitialize the weights of the entire CNN. Retrain the CNN on the classification task by using the new set of objects. Reinitialize the weights of the entire network. Retrain the entire network on the prediction task by using the new set of objects. Reinitialize the weights of the entire RNN. Retrain the entire model on the prediction task by using the new set of objects. Reinitialize the weights of the last fully connected layer of the CNN. Retrain the CNN on the classification task by using the new set of objects. Reinitialize the weights of the last layer of the RNN. Retrain the entire model on the prediction task by using the new set of objects.

A company distributes an online multiple-choice survey to several thousand people. Respondents to the survey can select multiple options for each question. A machine learning (ML) engineer needs to comprehensively represent every response from all respondents in a dataset. The ML engineer will use the dataset to train a logistic regression model. Which solution will meet these requirements?. Perform one-hot encoding on every possible option for each question of the survey. Perform binning on all the answers each respondent selected for each question. Use Amazon Mechanical Turk to create categorical labels for each set of possible responses. Use Amazon Textract to create numeric features for each set of possible responses.

A manufacturing company stores production volume data in a PostgreSQL database. The company needs an end-to-end solution that will give business analysts the ability to prepare data for processing and to predict future production volume based the previous year's production volume. The solution must not require the company to have coding knowledge. Which solution will meet these requirements with the LEAST effort?. Use AWS Database Migration Service (AWS DMS) to transfer the data from the PostgreSQL database to an Amazon S3 bucket. Create an Amazon EMR duster to read the S3 bucket and perform the data preparation. Use Amazon SageMaker Studio for the prediction modeling. Use AWS Glue DataBrew to read the data that is in the PostgreSQL database and to perform the data preparation. Use Amazon SageMaker Canvas for the prediction modeling. Use AWS Database Migration Service (AWS DMS) to transfer the data from the PostgreSQL database to an Amazon S3 bucket. Use AWS Glue to read the data in the S3 bucket and to perform the data preparation. Use Amazon SageMaker Canvas for the prediction modeling. Use AWS Glue DataBrew to read the data that is in the PostgreSQL database and to perform the data preparation. Use Amazon SageMaker Studio for the prediction modeling.

A data scientist needs to create a model for predictive maintenance. The model will be based on historical data to identify rare anomalies in the data. The historical data is stored in an Amazon S3 bucket. The data scientist needs to use Amazon SageMaker Data Wrangler to ingest the data. The data scientist also needs to perform exploratory data analysis (EDA) to understand the statistical properties of the data. Which solution will meet these requirements with the LEAST amount of compute resources?. Import the data by using the None option. Import the data by using the Stratified option. Import the data by using the First K option. Infer the value of K from domain knowledge. Import the data by using the Randomized option. Infer the random size from domain knowledge.

An ecommerce company has observed that customers who use the company's website rarely view items that the website recommends to customers. The company wants to recommend items to customers that customers are more likely to want to purchase. Which solution will meet this requirement in the SHORTEST amount of time?. Host the company's website on Amazon EC2 Accelerated Computing instances to increase the website response speed. Host the company's website on Amazon EC2 GPU-based instances to increase the speed of the website's search tool. Integrate Amazon Personalize into the company's website to provide customers with personalized recommendations. Use Amazon SageMaker to train a Neural Collaborative Filtering (NCF) model to make product recommendations.

A machine learning (ML) engineer is preparing a dataset for a classification model. The ML engineer notices that some continuous numeric features have a significantly greater value than most other features. A business expert explains that the features are independently informative and that the dataset is representative of the target distribution. After training, the model's inferences accuracy is lower than expected. Which preprocessing technique will result in the GREATEST increase of the model's inference accuracy?. Normalize the problematic features. Bootstrap the problematic features. Remove the problematic features. Extrapolate synthetic features.

A manufacturing company produces 100 types of steel rods. The rod types have varying material grades and dimensions. The company has sales data for the steel rods for the past 50 years. A data scientist needs to build a machine learning (ML) model to predict future sales of the steel rods. Which solution will meet this requirement in the MOST operationally efficient way?. Use the Amazon SageMaker DeepAR forecasting algorithm to build a single model for all the products. Use the Amazon SageMaker DeepAR forecasting algorithm to build separate models for each product. Use Amazon SageMaker Autopilot to build a single model for all the products. Use Amazon SageMaker Autopilot to build separate models for each product.

A machine learning (ML) specialist is building a credit score model for a financial institution. The ML specialist has collected data for the previous 3 years of transactions and third-party metadata that is related to the transactions. After the ML specialist builds the initial model, the ML specialist discovers that the model has low accuracy for both the training data and the test data. The ML specialist needs to improve the accuracy of the model. Which solutions will meet this requirement? (Choose two.). Increase the number of passes on the existing training data. Perform more hyperparameter tuning. Increase the amount of regularization. Use fewer feature combinations. Add new domain-specific features. Use more complex models. Use fewer feature combinations. Decrease the number of numeric attribute bins. Decrease the amount of training data examples. Reduce the number of passes on the existing training data.

A data scientist uses Amazon SageMaker to perform hyperparameter tuning for a prototype machine leaming (ML) model. The data scientist's domain knowledge suggests that the hyperparameter is highly sensitive to changes. The optimal value, x, is in the 0.5 < x < 1.0 range. The data scientist's domain knowledge suggests that the optimal value is close to 1.0. The data scientist needs to find the optimal hyperparameter value with a minimum number of runs and with a high degree of consistent tuning conditions. Which hyperparameter scaling type should the data scientist use to meet these requirements?. Auto scaling. Linear scaing. Logarithmic scaling. Reverse logarithmic scaling.

A data scientist uses Amazon SageMaker Data Wrangler to analyze and visualize data. The data scientist wants to refine a training dataset by selecting predictor variables that are strongly predictive of the target variable. The target variable correlates with other predictor variables. The data scientist wants to understand the variance in the data along various directions in the feature space. Which solution will meet these requirements?. Use the SageMaker Data Wrangler multicollinearity measurement features with a variance inflation factor (VIF) score. Use the VIF score as a measurement of how closely the variables are related to each other. Use SageMaker Data Wrangler Data Quality and Insights Report quick model visualization to estimate the expected quality of a model that is trained on the data. Use the SageMaker Data Wrangler multicollinearity measurement features with the principal component analysis (PCA) algorithm to provide a feature space that includes all of the predictor variables. Use the SageMaker Data Wrangler Data Quality and Insights Report feature to review features by their predictive power.

A business to business (B2B) ecommerce company wants to develop a fair and equitable risk mitigation strategy to reject potentially fraudulent transactions. The company wants to reject fraudulent transactions despite the possibility of losing some profitable transactions or customers. Which solution will meet these requirements with the LEAST operational effort?. Use Amazon SageMaker to approve transactions only for products the company has sold in the past. Use Amazon SageMaker to train a custom fraud detection model based on customer data. Use the Amazon Fraud Detector prediction API to approve or deny any activities that Fraud Detector identifies as fraudulent. Use the Amazon Fraud Detector prediction API to identify potentially fraudulent activities so the company can review the activities and reject fraudulent transactions.

A data scientist needs to develop a model to detect fraud. The data scientist has less data for fraudulent transactions than for legitimate transactions. The data scientist needs to check for bias in the model before finalizing the model. The data scientist needs to develop the model quickly. Which solution will meet these requirements with the LEAST operational overhead?. Process and reduce bias by using the synthetic minority oversampling technique (SMOTE) in Amazon EMR. Use Amazon SageMaker Studio Classic to develop the model. Use Amazon Augmented Al (Amazon A2I) to check the model for bias before finalizing the model. Process and reduce bias by using the synthetic minority oversampling technique (SMOTE) in Amazon EMR. Use Amazon SageMaker Clarify to develop the model. Use Amazon Augmented AI (Amazon A2I) to check the model for bias before finalizing the model. Process and reduce bias by using the synthetic minority oversampling technique (SMOTE) in Amazon SageMaker Studio. Use Amazon SageMaker JumpStart to develop the model. Use Amazon SageMaker Clarify to check the model for bias before finalizing the model. Process and reduce bias by using an Amazon SageMaker Studio notebook. Use Amazon SageMaker JumpStart to develop the model. Use Amazon SageMaker Model Monitor to check the model for bias before finalizing the model.

A company has 2,000 retail stores. The company needs to develop a new model to predict demand based on holidays and weather conditions. The model must predict demand in each geographic area where the retail stores are located. Before deploying the newly developed model, the company wants to test the model for 2 to 3 days. The model needs to be robust enough to adapt to supply chain and retail store requirements. Which combination of steps should the company take to meet these requirements with the LEAST operational overhead? (Choose two.). Develop the model by using the Amazon Forecast Prophet model. Develop the model by using the Amazon Forecast holidays featurization and weather index. Deploy the model by using a canary strategy that uses Amazon SageMaker and AWS Step Functions. Deploy the model by using an A/B testing strategy that uses Amazon SageMaker Pipelines. Deploy the model by using an A/B testing strategy that uses Amazon SageMaker and AWS Step Functions.

A finance company has collected stock return data for 5,000 publicly traded companies. A financial analyst has a dataset that contains 2,000 attributes for each company. The financial analyst wants to use Amazon SageMaker to identify the top 15 attributes that are most valuable to predict future stock returns. Which solution will meet these requirements with the LEAST operational overhead?. Use the linear leaner algorithm in SageMaker to train a linear regression model to predict the stock returns. Identify the most predictive features by ranking absolute coefficient values. Use random forest regression in SageMaker to train a model to predict the stock returns. Identify the most predictive features based on Gini importance scores. Use an Amazon SageMaker Data Wrangler quick model visualization to predict the stock returns. Identify the most predictive features based on the quick mode's feature importance scores. Use Amazon SageMaker Autopilot to build a regression model to predict the stock returns. Identify the most predictive features based on an Amazon SageMaker Clarify report.

A company is using a machine learning (ML) model to recommend products to customers. An ML specialist wants to analyze the data for the most popular recommendations in four dimensions. The ML specialist will visualize the first two dimensions as coordinates. The third dimension will be visualized as color. The ML specialist will use size to represent the fourth dimension in the visualization Which solution will meet these requirements?. Use the Amazon SageMaker Data Wrangler bar chart feature. Use Group By to represent the third and fourth dimensions. Use the Amazon SageMaker Canvas box plot visualization Use color and fill pattern to represent the third and fourth dimensions. Use the Amazon SageMaker Data Wrangler histogram feature Use color and fill pattern to represent the third and fourth dimensions. Use the Amazon SageMaker Canvas scatter plot visualization Use scatter point size and color to represent the third and fourth dimensions.

A clothing company is experimenting with different colors and materials for its products. The company stores the entire sales history of all its products in Amazon S3. The company is using custom-built exponential smoothing (ETS) models to forecast demand for its current products. The company needs to forecast the demand for a new product variation that the company will launch soon. Which solution will meet these requirements?. Train a custom ETS model. Train an Amazon SageMaker DeepAR model. Train an Amazon SageMaker К-means clustering model. Train a custom XGBoost model.

A company's machine learning (ML) specialist is building a computer vision model to classify 10 different traffic signs. The company has stored 100 images of each class in Amazon S3, and the company has another 10,000 unlabeled images. All the images come from dash cameras and are a size of 224 pixels × 224 pixels. After several training runs, the model is overfitting on the training data. Which actions should the ML specialist take to address this problem? (Choose two.). Use Amazon SageMaker Ground Truth to label the unlabeled images. Most Voted. Use image preprocessing to transform the images into grayscale images. Use data augmentation to rotate and translate the labeled images. Most Voted. Replace the activation of the last layer with a sigmoid. Use the Amazon SageMaker k-nearest neighbors (k-NN) algorithm to label the unlabeled images.

A data science team is working with a tabular dataset that the team stores in Amazon S3. The team wants to experiment with different feature transformations such as categorical feature encoding. Then the team wants to visualize the resulting distribution of the dataset. After the team finds an appropriate set of feature transformations, the team wants to automate the workflow for feature transformations. Which solution will meet these requirements with the MOST operational efficiency?. Use Amazon SageMaker Data Wrangler preconfigured transformations to explore feature transformations. Use SageMaker Data Wrangler templates for visualization. Export the feature processing workflow to a SageMaker pipeline for automation. Use an Amazon SageMaker notebook instance to experiment with different feature transformations. Save the transformations to Amazon S3. Use Amazon QuickSight for visualization. Package the feature processing steps into an AWS Lambda function for automation. Use AWS Glue Studio with custom code to experiment with different feature transformations. Save the transformations to Amazon S3. Use Amazon QuickSight for visualization. Package the feature processing steps into an AWS Lambda function for automation. Use Amazon SageMaker Data Wrangler preconfigured transformations to experiment with different feature transformations. Save the transformations to Amazon S3. Use Amazon QuickSight for visualization. Package each feature transformation step into a separate AWS Lambda function. Use AWS Step Functions for workflow automation.