Describe the Core Components of the Databricks Intelligence Platform, Including Mosaic AI, DeltaLive Tables, Lakeflow Jobs, Data Intelligence Engine, Delta Lake, Unity Catalog, and Databricks SQL. DATA-ANALYST-ASSOCIATE Practice Questions (Page 7)

Questions 31–34

1. 31application · medium

   A business intelligence team wants to connect their existing BI tool (like Tableau) to Databricks to run live queries on Delta tables. They also need to ensure that the BI tool respects the same data access permissions as other Databricks users. What should they use?

   • ALakeflow Jobs with an export to CSV
   • BMosaic AI with a REST API
   • CDelta Live Tables with a BI connector
   • DDatabricks SQL with a JDBC/ODBC connection and Unity Catalog permissions
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2. 32foundation · easy

   Which feature of Delta Live Tables helps ensure data quality in pipelines?

   • AAutomatic generation of SQL queries for data analysis.
   • BDeclarative expectations that can validate data and handle invalid records.
   • CAutomated scaling of compute resources based on workload.
   • DTime travel to query historical versions of data.
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4. 33application · medium

   A company has multiple workspaces in different cloud regions. They need to provide a single set of access permissions for data analysts across all workspaces, and they also want to ensure that when analysts query data using Databricks SQL, they see only the tables they are allowed to access. What should they configure?

   • ADelta Lake time travel to control which versions of tables analysts can query
   • BMosaic AI to create a unified data access layer for all workspaces
   • CSeparate access control lists in each workspace, and use Databricks SQL to enforce them
   • DUnity Catalog as the central governance layer, with permissions applied to tables and queries run through Databricks SQL
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5. 34expert · hard

   A data team has a complex pipeline with multiple tasks: some tasks are independent, while others depend on the output of previous tasks. They need to schedule these tasks to run at specific times, and they also need to handle failures gracefully with automatic retries. They are considering using Delta Live Tables for the entire pipeline. What is the best approach?

   • AUse Databricks SQL to schedule the tasks and handle retries
   • BUse Delta Live Tables for the data transformation tasks and Lakeflow Jobs to orchestrate the overall schedule and retries
   • CUse only Delta Live Tables, as it can handle all scheduling and retries automatically
   • DUse only Lakeflow Jobs, as it can handle all data transformations and dependencies
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