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Microsoft Implementing Data Engineering Solutions Using Azure Databricks Sample Questions:

Question 1

You have an Azure Databricks workspace that contains an all-purpose cluster named Cluster1.
You discover that out of- memory (OOM) errors intermittently cause jobs running on Cluster1 to fail.
You need to identify the root cause of the failures by analyzing the runtime execution behavior. What should you do? 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 a job in Lakeflow Jobs named Job1.
Job1 processes raw data files stored in Azure Storage.
New files arrive at unpredictable intervals.
You need to ensure that Job1 starts automatically when new files arrive and does NOT consume compute resources when no data is available.
Which type of job trigger should you use?

A. scheduled
B. file arrival
C. continuous
D. manual

Question 3

You have an Azure Databricks workspace.
You need to ingest streaming data from Azure Event Hubs by using Apache Spark Structured Streaming The solution must authenticate to Event Hubs and read the event payload.
How should you complete the PySpark code segment? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Question 4

You have an Azure Databricks workspace named Workspace1 that contains a takehouse and is enabled for Unity Catalog.
You have a connection to a Microsoft SQL Server database named DB1.
You need to expose the schemas and tables of DB1 to meet the following requirements:
* The schemas and tables can be queried in Databricks.
* The schemas and tables appear alongside other Unity Catalog objects.
* The data is NOT copied into Databricks-managed storage.
Solution: You create a new native catalog in Unity Catalog. Does this meet the goal?

A. Yes
B. No

Question 5

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

Solutions: