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Microsoft DP-750 Exam Syllabus Topics:

Microsoft Implementing Data Engineering Solutions Using Azure Databricks Sample Questions:

Question 1

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.

Question 2

You have an Azure Databricks workspace that contains the objects shown in the following table.
Name | Type
Catalog1 | Catalog
Schema1 | Schema
Sales1 | Table
Notebook1 | Notebook
Space1 | AI/BI Genie space
Users often use the following words to refer to a sale: transaction, event, order, and invoice.
You need to create a knowledge store. The solution must ensure that when the users use any of the words in Space1, Genie queries the Sales1 table. Any other Genie spaces must remain unaffected.
To which object should you add the instructions?

A. Space1
B. Notebook1
C. Schema1
D. Sales1

Question 3

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

Question 4

You have an Azure Databricks workspace.
Users report that a Databricks notebook that runs each day takes longer than expected to run.
When reading the Directed Acyclic Graph (DAG), you discover the following issues concerning the Apache Spark stage:
* Most tasks in the stage finish quickly.
* A few tasks in the stage run more slowly.
* The CPU is underutilized at the end of the stage.
* The slow tasks process many more input records.
* The stage is blocked while it waits for the few slow tasks.
What is the root cause of the issues?

A. caching
B. spilling
C. shuffling
D. skewing

Question 5

You have an Azure Databricks workspace.
Users report that a Databricks notebook that runs each day takes longer than expected to run.
When reading the Directed Acyclic Graph (DAG), you discover the following issues concerning the Apache Spark stage:
* Most tasks in the stage finish quickly.
* A few tasks in the stage run more slowly.
* The CPU is underutilized at the end of the stage.
* The slow tasks process many more input records.
* The stage is blocked while it waits for the few slow tasks.
What is the root cause of the issues?

A. caching
B. spilling
C. shuffling
D. skewing

Solutions: