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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 is enabled for Unity Catalog.
You plan to ingest data from CSV files stored in Azure Data Lake Storage Gen2. New rows are appended frequently.
You need to implement a data ingestion solution that meets the following requirements:
* New data must be available in near-real-time (NRT).
* The data must be stored in managed Delta tables.
* The solution must minimize custom code and maintenance effort.
What should you include in the solution?

A. an external table that references the CSV files
B. scheduled Apache Spark batch jobs
C. Auto Loader
D. an Azure Data Factory pipeline

Question 2

You have an Azure Databricks job named Job1 that contains an ingestion task named Task1 and transformation task named Task2. You need to ensure that if Task1 fails, the task retries automatically, and Task2 is prevented from running How should you configure Job1? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Question 3

You have an Azure Databricks workspace that is enabled for Unity Catalog.
You need to implement a daily batch data process that requires complex and highly customized Python transformations. The solution must minimize additional complexity.
What should you include in the solution?

A. a Databricks notebook triggered by a job
B. an Azure Data Factory pipeline that has data flows
C. a Lakeflow Spark Declarative Pipelines (SDP) pipeline
D. a continuous job in Lakeflow Jobs

Question 4

You have an Azure Databricks workspace that is enabled for Unity Catalog and contains a Delta table named Orders.
You load the Orders table into an Apache Spark DataFrame named df.
You need to create a DataFrame that excludes rows where the order amount is null.
Solution: You run the following expression.
df.filter(df.order_amount.isNotNull())
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.
* Fail the pipeline update for Table1 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( " rule1 " , " amount > 0 " )
D. @dlt.expect_all_or_drop({ " rule1 " : " amount > 0 " })

Solutions: