Microsoft DP-750 : Implementing Data Engineering Solutions Using Azure Databricks

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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 uses Databricks SQL.
You have a table named sales_goals_source that contains the following columns:
* Salesperson
* Item
* 2019
* 2020
* 2021
You need to transform the year columns into rows and return the columns Salesperson, Item, Year, and Value.
How should you complete the SQL statement? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
SELECT Salesperson, Item, Year, Value
FROM sales_goals_source
UNPIVOT
(
Value FOR [first dropdown] IN [second dropdown]
);

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

Question 4

You need to configure the telemetry pipeline to support the planned changes for pipeline orchestration and address the resiliency issues.
What should you do?

A. Use Lakeflow Jobs and define task dependencies between the ingestion, cleansing, and curation tasks.
B. Create a single Databricks notebook that contains all the ingestion, cleansing, and curation logic in sequential cells.
C. Use Azure Data Factory to orchestrate three separate Databricks notebook activities and define the task dependencies between each notebook activity.
D. Create three separate jobs and schedule them to run at staggered times.

Question 5

You have an Azure Databricks workspace named Workspace1 that contains a Git folder linked to a remote Git repository. The Git folder contains a Databricks notebook named Notebook1.
From the main branch, you create a feature branch named Branch1 and commit changes to Notebook1.
Another user commits changes to Notebook1 in main.
When you attempt to merge Branch1 into main, a merge conflict occurs.
You need to ensure that Notebook1 in main includes the changes from both branches.
What should you do?

A. Merge main into Branch1, resolve the conflicts, and then complete the merge of Branch1 into main.
B. Apply the changes directly to the main branch.
C. From Workspace1, clone the main branch as a new Git folder.
D. From Workspace1, clone Branch1 as a new Git folder.

Solutions: