Microsoft DP-750 : Implementing Data Engineering Solutions Using Azure Databricks

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Microsoft DP-750 Exam Syllabus Topics:

Microsoft Implementing Data Engineering Solutions Using Azure Databricks Sample Questions:

1. You have an Azure Databricks workspace that contains a job in Lakeflow Jobs named Job1.
Job1 runs every hour.
Occasionally, Job1 takes longer than one hour to complete.
You need to configure the job scheduling behavior to meet the following requirements:
* Overlapping runs must be prevented to avoid data corruption.
* Scheduled runs must not be discarded when another run is already active.
What should you configure? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

2. You need to recommend a compute type for the production ingestion workloads and BI workloads. The solution must meet the environment and compute requirements.
What should you recommend for each type of workload? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

3. You have an Azure Databricks workspace named Workspace1 that contains a cluster named Cluster1.
You need to enable a user named User1 to install a Maven package on all the nodes of Cluster1. The solution must follow the principle of least privilege.
What should you do?

A) Grant User1 the CAN RESTART permission for Cluster1.
B) Assign User1 the Contributor role for the resource group that contains Workspace1.
C) Assign User1 the workspace admins role for Workspace1.
D) Grant User1 the CAN MANAGE permission for Cluster1.

4. You have an Azure Databricks workspace that is enabled for Unity Catalog.
You have a Lakeflow Spark Declarative Pipelines (SDP) pipeline that writes numerical data to a table named Table1 by using a data quality validation rule named rule1.
You need to modify rule1 to meet the following requirements:
Ensure that amount is always greater than 0.
Prevent an update to Table1 from being committed when data that violates rule1 is detected.
Which statement should you execute?

A) @dlt.expect_or_fail( " rule1 " , " amount > 0 " )
B) @dlt.expect_all_or_drop({ " rule1 " : " amount > 0 " })
C) @dlt.expect_or_drop( " rule1 " , " amount > 0 " )
D) @dlt.expect( " rule1 " , " amount > 0 " )

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

Solutions: