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 is enabled for Unity Catalog.
You need to share curated data with an external organization. The solution must meet the following requirements:
* The organization will use its own compute platform to query the data.
* Access to the data must be centrally governed by using Unity Catalog.
* Administrative effort must be minimized.
What should you do?

A) Create a SQL warehouse for external users.
B) Grant workspace access to external users.
C) Create a Lakeflow Connect pipeline.
D) Use Delta Sharing.
E) Create a pipeline to move the data to an SFTP server.

2. You have an Azure Databricks workspace that is enabled for Unity Catalog and contains a catalog named finance, finance contains two schemas named default and procurement.
You need to create a table named assets in the procurement schema, assets must contain the following columns:
* asset.id
* asset, type
* asset_name
How should you complete the SQL statement? To answer, drag the appropriate values to the correct targets.
Each value may be used once, more than once, or not at all You may need to drag the split bar between panes or scroll to view content NOTE: Each correct selection is worth one point.

3. You have an Azure Databricks workspace that contains a Delta table named Customer.
A job named Job1 performs frequent upserts into Customer.
You discover that Job1 has created many small Parquet files in Customer, and the small files are degrading query performance.
You need to improve query performance for the current data already stored in Customer. The solution must not affect the travel for the Customer table.
What should you do?

A) Run the OPTIMIZE command on the Customer table.
B) Set the delta.deletedFileRetentionDuration table property to 1 day.
C) Set the delta.autoOptimize.optimizeWrite Apache Spark configuration to true.
D) Run the VACUUM command on the Customer table.

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 contains a job in Lakeflow Jobs named Job1.
Job! runs every hour.
Occasionally, the job run takes longer than one hour to complete. Overlapping runs must be prevented to avoid data corruption.
You need to configure the job scheduling behavior.
What should you configure? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Solutions: