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Microsoft DP-750 Exam Syllabus Topics:

Microsoft Implementing Data Engineering Solutions Using Azure Databricks Sample Questions:

1. You need to configure compute for the ingestion of telemetry data. The solution must meet the data ingestion and processing requirements.
What should you do?

A) Disable autoscaling for a job compute cluster.
B) Increase an all-purpose cluster to a larger fixed node type.
C) Move the ingestion pipelines to shared compute.
D) Enable Photon acceleration for a job compute cluster.

2. You have an Azure Databricks workspace
You are creating a Lakeflow Spark Declarative Pipelines (SDP) pipeline that scales automatically. You need to configure compute for the pipeline. The solution must minimize operational costs and effort. What should you use?

A) a single-node, all-purpose cluster
B) the existing SQL warehouse
C) a job cluster that uses autoscaling
D) an all-purpose cluster that uses autoscaling

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

4. 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.

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.
* Fail the pipeline update for Table1 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: