A data engineer has multiple Unity Catalog-managed Delta tables that require regular maintenance. Currently, the engineer manually schedules OPTIMIZE and VACUUM jobs for each table, adjusting their frequency according to how often each table is queried and updated.
The engineer needs to eliminate this manual maintenance overhead and allow Databricks to determine automatically when and how to run these operations.
Which action should the engineer take?

• A.Enable liquid clustering with CLUSTER BY on each table so it automatically compacts files and removes stale files on a schedule.
• B.Set delta.autoOptimize.autoCompact and delta.autoOptimize.optimizeWrite table properties to trigger automatic VACUUM and file compaction.
• C.Enable predictive optimization at the account, catalog, or schema level so Databricks automatically identifies and runs maintenance operations on managed tables.
• D.Configure Auto Loader with schema evolution enabled so it automatically triggers OPTIMIZE and VACUUM after each ingestion batch.

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A data engineer wants to create an external table in Databricks that references data stored in an Azure Data Lake Storage (ADLS) location. The goal is to enable Databricks to access and query this external data without moving it into Databricks-managed storage.
Which step should the data engineer take to successfully create the external table?

• A.Use the CREATE TABLE statement and specify the LOCATION clause with the path to the external data.
• B.Use the CREATE UNMANAGED TABLE statement without specifying a LOCATION clause.
• C.Use the CREATE EXTERNAL TABLE statement without specifying a LOCATION clause.
• D.Use the CREATE MANAGED TABLE statement and specify the LOCATION clause with the path to the external data.

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A data engineer wants to update specific rows in a Delta table based on matching keys from another dataset. The operation must support both insert and update logic in a single statement.
Which Delta Lake command supports this functionality?

• A.UPDATE
• B.MERGE INTO
• C.DELETE
• D.INSERT INTO

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Which of the following is hosted completely in the control plane of the classic Databricks architecture?

• A.Worker node
• B.JDBC data source
• C.Databricks web application
• D.Databricks Filesystem
• E.Driver node

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A Delta Live Table pipeline includes two datasets defined using STREAMING LIVE TABLE.
Three datasets are defined against Delta Lake table sources using LIVE TABLE.
The table is configured to run in Development mode using the Continuous Pipeline Mode.
Assuming previously unprocessed data exists and all definitions are valid, what is the expected outcome after clicking Start to update the pipeline?

• A.All datasets will be updated once and the pipeline will shut down. The compute resources will persist to allow for additional testing.
• B.All datasets will be updated once and the pipeline will persist without any processing. The compute resources will persist but go unused.
• C.All datasets will be updated at set intervals until the pipeline is shut down. The compute resources will persist to allow for additional testing.
• D.All datasets will be updated at set intervals until the pipeline is shut down. The compute resources will persist until the pipeline is shut down.
• E.All datasets will be updated once and the pipeline will shut down. The compute resources will be terminated.

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