Online Access Free Databricks-Certified-Data-Engineer-Associate Practice Test

A data engineer runs VACUUM orders RETAIN 72 HOURS on a Delta table and the command fails with an error about the retention interval.
What is the most likely cause?

• A.VACUUM requires the table to be an external table.
• B.The specified retention is shorter than the default 7-day threshold, which is blocked by the retention duration check.
• C.The table must be optimized with ZORDER before vacuuming.
• D.VACUUM cannot be run on tables with a defined partition column.

A streaming pipeline reads IoT sensor data and writes aggregated results to a Delta table. The engineer wants to ensure that late-arriving records within a certain time window are still processed correctly. Which Structured Streaming feature should be used?

• A.Watermarking
• B.Caching
• C.Partitioning
• D.Broadcast join

Which Databricks feature can be used to check the data sources and tables used in a workspace?

• A.Use the lineage feature to visualize a graph that highlights where the table is used only in notebooks.
• B.Use the lineage feature to visualize a graph that highlights where the table is used only in reports.
• C.Use the audit logs feature to review user activities, such as who queried or modified a table, instead of visualizing dependencies between data objects.
• D.Use the lineage feature to visualize a graph that shows all dependencies, including where the table is used in notebooks, other tables, and reports.

A data engineer at a company that uses Databricks with Unity Catalog needs to share a collection of tables with an external partner who also uses a Databricks workspace enabled for Unity Catalog. The data engineer decides to use Delta Sharing to accomplish this. What is the first piece of information the data engineer should request from the external partner to set up Delta Sharing?

• A.The name of their Databricks cluster
• B.Their Databricks account password
• C.The IP address of their Databricks workspace
• D.The sharing identifier of their Unity Catalog metastore

A data engineering team must version-control notebooks, review changes through pull requests, and deploy the same code to dev, staging, and production workspaces.
Which Databricks capability supports this workflow?

• A.Storing notebook source files in DBFS /FileStore and copying them manually
• B.Exporting notebooks as DBC archives and emailing them between workspaces
• C.Databricks Git folders (Repos) integrated with a remote Git provider
• D.Enabling notebook revision history and restoring snapshots as needed