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Microsoft DP-750 Q&A - in .pdf
- Exam Code: DP-750
- Exam Name: Implementing Data Engineering Solutions Using Azure Databricks
- Updated: Oct 04, 2026
- Q & A: 93 Questions and Answers
- Convenient, easy to study.
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- Exam Code: DP-750
- Exam Name: Implementing Data Engineering Solutions Using Azure Databricks
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Microsoft DP-750 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Deploy and maintain data pipelines and workloads | 30β35% | - Build and orchestrate pipelines
|
| Secure and govern Unity Catalog objects | 15β20% | - Implement data governance and security
|
| Prepare and process data | 30β35% | - Optimize and manage data storage
|
| Set up and configure an Azure Databricks environment | 15β20% | - Integrate with Azure services
|
Microsoft Implementing Data Engineering Solutions Using Azure Databricks Sample Questions:
Question #1
You have an Azure Databricks workspace that contains a job in Lakeflow Jobs named Job1.
Job! contains three tasks named Task1, Task2. and Task3.
If Task1 fails, Task2 and Task3 must be prevented from running. Successfully completed tasks must NOT rerun during recovery.
You need to configure Job1 to support controlled failure handling and recovery What should you configure? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Correct Answer:
Explanation:
Two configurations are needed:
Task dependency with ' All succeeded ' run condition: Set Task2 and Task3 to depend on Task1. Change the run condition on Task2 and Task3 to ' All succeeded ' - this means they only execute when all their upstream dependencies (Task1) have succeeded. If Task1 fails, both downstream tasks are skipped automatically, not run with failed inputs.
Repair run for recovery: Lakeflow Jobs ' Repair Run feature lets you re-execute only the tasks that failed (Task1 in this case) and their dependents (Task2 and Task3 if they were skipped), while skipping Task1 and any other tasks that already completed successfully. Successfully completed tasks are never re-executed during repair - their results are reused as-is.
Together these provide both controlled failure propagation (nothing runs downstream of a failure) and efficient recovery.
Reference: https://learn.microsoft.com/en-us/azure/databricks/jobs/repair-job-failures
Question #2
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]
);
Correct Answer:
Explanation:
First dropdown: Year
Second dropdown: (2019, 2020, 2021)
The UNPIVOT operator converts the separate 2019, 2020, and 2021 columns into rows. Value becomes the output column containing the values previously stored in those year columns. Year becomes the output name column that identifies the original column from which each value came. Therefore, the expression must use Value FOR Year IN (2019, 2020, 2021). The Salesperson and Item columns are not included in the IN list because they remain identifier columns and are repeated for every resulting year row. A single source row consequently produces three output rows-one for each listed year. Selecting (Year) would reference an output name rather than the source columns that must be rotated.
Question #3
You have an Azure Databricks workspace named Workspace1 that contains a cluster named Cluster1.
You need to enable a user named User1 to install a Maven package on all the nodes of Cluster1. The solution must follow the principle of least privilege.
What should you do?
- A. Assign User1 the Contributor role for the resource group that contains Workspace1.
- B. Assign User1 the workspace admins role for Workspace1.
- C. Grant User1 the CAN RESTART permission for Cluster1.
- D. Grant User1 the CAN MANAGE permission for Cluster1.
Correct Answer: D π³οΈ
Explanation: Only visible for PracticeDump members. You can sign-up / login (it's free).
Question #4
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.
Correct Answer:
Explanation:
Diagnosing OOM errors requires analysing actual runtime execution behaviour. The Spark UI is the primary tool - it ' s accessible from the cluster detail page and captures rich per-stage and per-task metrics without any extra setup.
In the Executors tab, look at storage memory used, execution memory used, memory spill to disk, and GC time per executor. An executor showing high memory spill is a strong indicator that it ' s processing more data than it can hold in memory - often caused by data skew, where one partition is far larger than the others.
The Stages tab shows task distribution - if one task in a stage is processing 10x more data than its peers, that
' s data skew causing memory pressure on that specific executor. Ganglia (available on older runtimes) provides node-level OS metrics like heap usage over time, which can confirm whether memory pressure is sustained or spiky.
These built-in tools give a complete picture of the root cause before making any configuration changes.
Reference: https://learn.microsoft.com/en-us/azure/databricks/compute/monitor-cluster
Question #5
You have an Azure Databricks workspace that is enabled for Unity Catalog and contains:
* A catalog named Finance
* A schema named Purchases in the Finance catalog
* A table named Transactions in the Purchases schema
You need to ensure that a user named finance_user can query the Transactions table. The solution must follow the principle of least privilege.
Which permission should you grant to finance_user for each object? To answer, drag the appropriate permissions to the correct objects. Each permission may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.
Correct Answer:
* The Finance catalog: USE CATALOG
* The Purchases schema: USE SCHEMA
* The Transactions table: SELECT
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