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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 named Workspace1 that contains a lakehouse 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 Databricks access connector.
Does this meet the goal?

A) No
B) Yes

2. You have an Azure Databricks workspace that uses Unity Catalog.
You have a Lakeflow Spark Declarative Pipelines (SDP) pipeline that ingests data into a managed Delta table named Table1. Table! is used for analytics.
New columns are added to the source data, causing pipeline failures during writes to Table!
You need to prevent the pipeline failures. The solution must ensure that schema changes are detected and handled.
What should you do?

A) Use row filters to exclude records that have new columns.
B) Enable schema evolution.
C) Disable schema enforcement for Table1.
D) Create a separate table for each schema version.

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

4. You have an Azure Databricks workspace that is enabled for Unity Catalog.
You have a Lakeflow Spark Declarative Pipelines (SDP) pipeline that writes records to a Delta table named Table1 by using a data quality rule named rule1 You need to meet the following requirements:
* Records that violate rule! must NOT be written to Table1. but the pipeline must continue processing valid records.
* Data engineers must be able to review expectation metrics by using minimal development effort.
What should you do? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

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

For each of the following statements, select Yes if the statement is true. Otherwise, select No. NOTE: Each correct selection is worth one point.

Solutions: