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Microsoft Implementing Data Engineering Solutions Using Azure Databricks Sample Questions:

1. Hotspot Question
You have an Azure Databricks workspace that is enabled for Unity Catalog.
You plan to run the following PySpark code.

2. You have an Azure Databricks workspace that is enabled for Unity Catalog.
You have an Apache Spark Structured Streaming job that writes data to a Delta table.
After the cluster restarts, the streaming job reprocesses previously ingested data.
You need to prevent the streaming job from reprocessing the data after the cluster restarts.
What should you do?

A) Configure a watermark for the streaming query.
B) Increase the trigger interval of the streaming query.
C) Enable change data feed (CDF) for the target table.
D) Configure a checkpoint location for the streaming query.

3. Which feature provides end-to-end tracking of data movement in Databricks?

A) Spark logs
B) Query history only
C) Cluster metrics
D) Unity Catalog lineage

4. You have an Azure Databricks workspace that contains a Git folder and uses Azure Repos as the Git provider.
From the main branch, you create a branch named Branch1. You commit changes to Branch1.
You need to incorporate the changes from Branch1 into main. The solution must preserve the commit history in the repository.
Which command should you run?

A) rebase
B) merge
C) pull
D) push

5. Hotspot Question
You have an Azure Databricks workspace that contains a job in Lakeflow Jobs named Job1.
Job1 contains three tasks named Task1, Task2, and Task3.
If Task1 fails, Task2 and Task3 must be prevented from running. Successfully completed tasks must NOT rerun during recovery.
You need to configure Job1 to support controlled failure handling and recovery.
What should you configure? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Solutions: