1Z0-1096-24 Skill Checks and Practice Exam

What are two key features of Machine Learning?. Prevents data loss and down time. Analyzes small volumes of data. Supports a range of techniques including classification, regression, and clustering. Enables discovery of patterns without explicit programming.

What is Machine Learning?. A utility for unloading data and metadata into a set of operating system files called a dump file set. A field of AI that involves developing algorithms and statistical models that enable learning from and making predictions or decisions using data, without being explicitly programmed. A comprehensive set of services that create, maintain, manage, and monitor one or more standby databases to enable production Oracle databases to survive disasters and data corruptions. A feature of vector databases for performing semantic similarity search.

What are two advantages of Oracle Machine Learning for Python?. Operates on database data from Python without using SQL. Maximizes data movement. Supports the use of additional Python packages to complement in-database functionality through embedded Python execution. Cannot automate common Machine Learning tasks.

Which two can you access from the Oracle Machine Learning home page?. Your Autonomous Database instance. Quick links to important interfaces. A log of your recent high-level activities. Database actions.

What is a notebook?. It is a collaborative interface where you can write SQL, R, and Python code and document your work using markdown. It contains all the elements associated with a project. It is the space within OML where your projects reside. It lists all the jobs created, along with the job name, owner of the job, last start date, next run date, status, and schedule.

Which three types of permissions can a user be granted to a workspace?. Contributor. Manager. Developer. Viewer. Member. Downloader.

Which four languages are supported by Oracle Machine Learning?. PL/SQL. Swift. C. SQL. R. Python.

Oracle Autonomous Database is designed for the administration of a database with SQL commands executed by using Machine Learning. TRUE. FALSE.

Which two components on Autonomous Database Serverless can be used to develop and deploy SQL, R, and Python code?. OML Notebooks. Workspaces. Projects. SQL Developer Web.

Which step in the Oracle AutoML pipeline aims to find the smallest sample size that adequately represents the full data set?. Algorithm Ranking. Adaptive Sampling. Feature Selection. Model Tuning.

Which step in the Oracle AutoML pipeline determines the settings or hyperparameters that produce a better quality model within the constraints, such as time, specified?. Algorithm Ranking. Adaptive Sampling. Feature Selection. Model Tuning.

Which class would you use to automatically select the best Oracle Machine Learning algorithm and tune the model?. oml.automl.ModelSelection. oml.automl.ModelTuning. oml.automl.FeatureSelection. oml.automl.AlgorithmSelection.

You must bind a notebook to an interpreter to fetch data from a database or any data source. Which is the default interpreter?. Low. Medium. High. Extreme.

Which statement is false?. You can create a backup of a notebook. You can delete the selected version of your notebook. You can create a new notebook from the selected notebook version. You cannot restore your notebook to the older version.

For which of the following is OML Notebooks NOT a web-based interface?. Data analysis. Data discovery. Data insights. Data visualization. Collaboration.

Which is NOT one of the three types of templates?. Personal templates. Shared templates. Example templates. Fixed templates.

Which task cannot be performed with personal templates?. Run all paragraphs in the template notebook. Rename the template notebook. Create a notebook from the available templates. Delete selected notebook templates. Save selected notebook as a shared template.

Which is NOT an action that results in the Shared Templates page tracking notebook templates?. Liking templates. Deleting templates. Creating notebooks from templates. Viewing templates.

What is the maximum character limit for a job name?. 64 bytes. 100 bytes. 128 bytes. 130 bytes.

Which is NOT viewable on the Jobs page?. Jobs log. All the jobs created. The next run date. Job status.

Which statement is NOT true about jobs?. On the Jobs page, you can create, duplicate, stop, and delete jobs. You can edit an existing job. You can version jobs. Jobs enable you to create schedules to run notebooks.

Data monitoring is only useful in the context of machine learning. TRUE. FALSE.

Which statement about model monitoring is NOT true?. Model monitoring helps detect deteriorating model accuracy and provide insights into underlying causes. Model monitoring uses model accuracy trends and model feature impact to guide model rebuilding. Model monitoring automatically addresses data quality issues affecting model accuracy.

When creating a model monitor through the UI, you can specify data monitoring as well. TRUE. FALSE.

An administrator can create, edit, and remove user accounts in OML. TRUE. FALSE.

Which statement about the Compute Resources page is NOT true?. Compute resource refers to the services to which a notebook Interpreter connects. The Compute Resources page displays the list of compute resources. The Compute Resources page displays the name of each resource and last-updated details. You do not need the Administrator role to access the Compute Resources page.

Which task can be performed by a developer?. Create a user account and password. Create and schedule jobs. Create connection groups to allow connectivity to the database. Remove all users.

You have produced several regression models, and now you are asked to select the best model of the set. Which two types of statistics would you use to evaluate regression models?. Root mean squared error. Mean absolute error. True positive rate. Confusion matrix statistics.

Which two statements are FALSE regarding the OML Services REST API access token?. The token is tied to the OML user who authorizes with the OML user credential. The token is valid for 1 hour. An expired token can be refreshed. Each token can be used many times. A token can be refreshed up to 8 hours. A revoked token can be refreshed. A token can be revoked.

Which four parameters are supported/valid to use in oml.connect(……) provided by OML4Py to establish an OML4Py connection to an Oracle Database instance?. USER. PASSWORD. DSN. AUTOML. RESTRICTION. WALLET.

A customer's data science team consists of both R users with on-premises development and cloud production environments. Data is periodically synchronized between the Oracle Base Database Service and on-premises Oracle Database instances. Which scenario do you recommend to support their project for performance, scalability, and maintainability while maintaining data security?. Use OML4R with Oracle Database on premises to develop and test R user-defined functions, then test and deploy these scripts in Oracle Base Database Service. Use the SQL API to invoke the user-defined R functions from applications. Use open source R with ROracle to extract database data and develop scripts to meet project goals. Manually spawn R engines from the application to run these scripts in production. Use OML4R with Oracle Database on premises to develop and test user-defined R functions, then use open source R with ROracle to connect to Oracle Base Database Service for testing and deploying these scripts. Use open source R with ROracle to extract database data and develop scripts to meet project goals. Then, store these same scripts in the Oracle Base Database Service R script repository. In R, load these scripts into the R engine for execution.

You need to compute the correlation of two numeric columns X, Y in a database table referenced by a data frame proxy object DF. The correlation here is defined as E[(X – mean(X))*(Y – mean(Y))]/cov(X,Y). What are two ways to do it in OML4R and OML4Py?. In OML4Py, DF['X', 'Y'].corr(method = 'pearson'). In OML4R, ore.corr(DF,var='X,Y', stats = 'spearman'). In OML4R, ore.corr(DF,var='X,Y', stats = 'pearson'). In OML4Py, DF['X', 'Y'].corr(method = 'spearman').

On Autonomous Database, you are using OML Notebooks and want to schedule a recurring notebook execution. The lead data scientist wants the script run five times every other day starting today at 11:00 PM, but only retry twice if it should fail, and pad the timeout to account for possible longer execution times. Normally, the execution takes 20 minutes. Repeat Frequency: 2 days Maximum Number of Runs: 5 Maximum Failures Allowed: 2 Timeout in minutes: 40 minutes. Repeat Frequency: 5 days Maximum Number of Runs: 2 Maximum Failures Allowed: 2 Timeout in minutes: 20 minutes. Repeat Frequency: 2 days Maximum Number of Runs: 5 Maximum Failures Allowed: 2 Timeout in minutes: 10 minutes. Repeat Frequency: 2 weeks Maximum Number of Runs: 2 Maximum Failures Allowed: 5 Timeout in minutes: 60 minutes.

Where are notebooks managed by OML Notebooks stored for a given user?. Oracle Cloud Infrastructure Object storage. Oracle Cloud Infrastructure File storage. Oracle Cloud Infrastructure Block storage. Oracle Cloud Infrastructure Archive storage. Autonomous Database user's own schema.

Which four classification algorithms are supported in Oracle Data Miner?. Decision Tree. K-Nearest Neighbors. Generalized Linear Model. Factor Analysis. NaïBayes. Support Vector Machine. Neural Networks.

Which is a FALSE statement regarding Oracle Machine Learning (OML)?. OML provides univariate and multivariate statistics. OML provides scalable statistical functions through OML4Py and OML4R. OML provides integration with open source Python and R statistical analysis functions. OML needs a separate data visualization tool for creating visualizations.

Which statement about automl.FeatureSelection in OML4Py is correct syntax?. from oml.automl import FeatureSelection fs = FeatureSelection(mining_function = 'classification', score_metric = 'accuracy'). from oml.automl import FeatureSelection fs = FeatureSelection(mining_function = 'classification', metric = 'accuracy'). from oml.automl import FeatureSelection fs = FeatureSelection(mining_function = 'clustering', score_metric = 'accuracy'). from oml.automl import FeatureSelection fs = FeatureSelection(mining_function = 'clustering', metric = 'accuracy').

You create an in-database Decision Tree model using OML4R, and you want to review any of the leaf nodes in the tree that have more than 50 records. How can you achieve this using OML4R?. dt_mod <- ore.odmDT(target ~ ., INPUT_DATA) dt_mod$nodes[dt_mod$nodes$row.count > 50,]. dt_mod <- ore.odmDT(target ~ ., INPUT_DATA) predict(dt_mod$nodes$row.count > 50). dt_mod <- ore.odmDT(target ~ ., INPUT_DATA, nodes = 50) summary(dt_mod). dt_mod <- ore.odmDT(target ~ ., INPUT_DATA, nodes = 50) summary(dt_mod$nodes).

What is the primary function of OML Services?. To provide cloud storage solutions. To manage user access and permissions. To deploy and manage machine learning models using REST endpoints. To offer web hosting services.

Given a table mining_data_test with columns CUST_ID, YRS_RESIDENCE, CUST_GENDER, and AFFINITY_CARD. You built a classification model using YRS_RESIDENCE and CUST_OWN_OR_RENT to predict AFFINITY_CARD. Which three code sequences compute the confusion matrix after the model is built?. In OML4SQL, the model is built with the name nb_sh_clas_sample CREATE TABLE nb_apply_results AS SELECT cust_id, PREDICTION(nb_sh_clas_sample USING *) prediction, PREDICTION_PROBABILITY(nb_sh_clas_sample USING *) probability FROM mining_data_test; BEGIN DBMS_DATA_MINING.COMPUTE_CONFUSION_MATRIX ( accuracy => v_accuracy, apply_result_table_name => 'nb_apply_results', target_table_name => 'mining_data_test', case_id_column_name => 'cust_id', target_column_name => 'affinity_card', confusion_matrix_table_name => 'nb_confusion_matrix', score_column_name => 'PREDICTION', score_criterion_column_name => 'PROBABILITY' score_criterion_type => 'PROBABILITY'); END; SELECT * from nb_confusion_matrix;. In OML4Py, the model is built as dt_mod RES_DF = dt_mod.predict(mining_data_test [['YRS_RESIDENCE', 'CUST_OWN_OR_RENT']], supplemental_cols = mining_data_test ['AFFINITY_CARD']) RES_DF.crosstab('AFFINITY_CARD', 'PREDICTION'). In OML4R, the model is built as dt_mod mining_data_test$PRED <- ore.predict(dt_mod, mining_data_test) table(mining_data_test$PRED, mining_data_test$AFFINITY_CARD). In OML4Py, the model is built as dt_mod RES_DF = dt_mod.predict(mining_data_test [['YRS_RESIDENCE', 'CUST_OWN_OR_RENT']], supplemental_cols = mining_data_test ['AFFINITY_CARD'], proba = TRUE) RES_DF.crosstab('AFFINITY_CARD', 'PROBABILITY').

For which reason does a data scientist perform "feature engineering" when building models?. Data scientists need clean data and prepared data. Feature engineering, required for the algorithms, removes missing values and normalizes data so the features are between -1 to 1 or 0 to 1 ranges. Machine learning algorithms require a reduced and prioritized collection of input features. Oracle Machine Learning uses the Minimum Description Length (MDL) algorithm for feature selection (also known as attribute importance). Machine learning algorithms require a reduced and prioritized collection of input attributes. Oracle Machine Learning uses the Non-Negative Matrix Factorization (NMF) algorithm for feature extraction. Feature engineering derives new features from existing features to build better models. For example to target High_Life_Time_Value Customers, rather than using Purchase_Amount, you could create a new attribute: "Count_When_Purchase_Exceeds_$500_in_12_month_period.".

Which OML component on Autonomous Database provides a REST API?. OML4Py. OML4SQL. Oracle Data Miner. Oracle Data Pump.

When managing models using the OML Models interface on Autonomous Database, what are the three operations a user can perform on models?. Deploy an existing in-database model as a REST endpoint in OML Services. Delete an existing in-database model. Undeploy a previously deployed in-database model. Change the namespace of a previously deployed in-database model. Change the owner of a previously deployed in-database model. Change the deployment date of a previously deployed in-database model.

Which two Oracle Data Miner nodes focus on data exploration and visualization?. SQL Query node. Explore Data node. Analyze node. Graph node. Class Build node. Data Visualization node.

In the OML Model Monitoring UI, which two of the following are not a prediction statistic provided by the monitor?. Population Stability Index. Standard Deviation. Variance. Mean. Median.