Exam Name: Implementing Data Engineering Solutions Using Azure Databricks
Certification Provider: Microsoft
Corresponding Certification: Microsoft Certified: Fabric Data Engineer Associate
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Last Updated: Oct 18, 2026
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Microsoft DP-750 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Deploy and maintain data pipelines and workloads | 30–35% | - Monitor, troubleshoot, and maintain workloads
|
| Secure and govern Unity Catalog objects | 15–20% | - Implement data governance and security
|
| Prepare and process data | 30–35% | - Ingest and transform data
|
| Set up and configure an Azure Databricks environment | 15–20% | - Integrate with Azure services
|
Microsoft Implementing Data Engineering Solutions Using Azure Databricks Sample Questions:
You have an Azure Databricks workspace that is enabled for Unity Catalog.
You need to implement a data lifecycle and expiration solution that meets the following requirements
* Transaction logs and deleted data files that are older than 90 days must be removed from Delta tables to reclaim storage.
* All the tables must remain available for querying during the cleanup process.
* Administrative effort must be minimized
What should you do for each requirement? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Correct Answer:
Explanation:
Two actions are needed to reclaim storage while keeping tables queryable:
Set delta.deletedFileRetentionDuration and delta.logRetentionDuration to 90 days on each table. These properties define the retention floor - VACUUM will not touch anything newer than this threshold, so no data needed for time travel within 90 days can be accidentally removed.
Run VACUUM on each table. VACUUM is the Delta Lake command that physically removes data files and transaction log entries older than the retention duration. Importantly, VACUUM runs as a background operation - it uses Delta Lake ' s MVCC (multi-version concurrency control) to ensure that concurrent reads against the table continue uninterrupted while cleanup happens. Tables are fully available throughout.
OPTIMIZE compacts small files for query performance but doesn ' t delete anything. Manually deleting files outside the Delta protocol would corrupt the table.
Reference: https://learn.microsoft.com/en-us/azure/databricks/sql/language-manual/delta-vacuum
You have an Azure Databricks workspace.
Users report that a Databricks notebook that runs each day takes longer than expected to run.
When reading the Directed Acyclic Graph (DAG), you discover the following issues concerning the Apache Spark stage:
* Most tasks in the stage finish quickly.
* A few tasks in the stage run more slowly.
* The CPU is underutilized at the end of the stage.
* The slow tasks process many more input records.
* The stage is blocked while it waits for the few slow tasks.
What is the root cause of the issues?
- A. skewing
- B. shuffling
- C. spilling
- D. caching
Correct Answer: A 🗳️
Which SCD type should you use to support the planned data modeling changes? To answer, drag the appropriate types to the correct issues. Each type 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:
Explanation:
The correct mapping is SCD Type 1 for equipment metadata and SCD Type 2 for IoT sensor ownership history.
SCD Type 1 overwrites the existing record whenever an attribute changes - no history is kept. Contoso ' s requirement for equipment metadata (name, manufacturer, model, commissioning date) states ' historical values are NOT required, ' which is the textbook definition of Type 1. A MERGE INTO with WHEN MATCHED THEN UPDATE handles this cleanly in Delta Lake.
SCD Type 2 creates a new row for each change, preserving the full history through effective-date or version columns. Contoso requires that ' analysts must track the full history of ownership ' as sensors change hands over time - that full audit trail is only possible with Type 2. Type 3 (keeping just the previous value in an extra column) would lose earlier ownership records, so it doesn ' t satisfy the ' full history ' requirement.
Reference: https://learn.microsoft.com/en-us/azure/databricks/delta/merge
You have an Azure Databricks workspace that is enabled for Unity Catalog.
You have 500 GB of sales data stored as multiple CSV files in cloud storage.
You plan to load the data into a Delta table.
You need to ingest the bulk data by using a solution that meets the following requirements:
* Minimize how long it takes to implement the solution.
* Minimize the amount of custom code required.
What should you use?
- A. Auto Loader
- B. file upload
- C. Apache Spark read APIs
- D. COPY INTO
Correct Answer: D 🗳️
Explanation: Only visible for Fast2test members. You can sign-up / login (it's free).
You have an Azure Databricks workspace.
Users report that a Databricks notebook that runs each day takes longer than expected to run.
When reading the Directed Acyclic Graph (DAG), you discover the following issues concerning the Apache Spark stage:
* Most tasks in the stage finish quickly.
* A few tasks in the stage run more slowly.
* The CPU is underutilized at the end of the stage.
* The slow tasks process many more input records.
* The stage is blocked while it waits for the few slow tasks.
What is the root cause of the issues?
- A. skewing
- B. shuffling
- C. spilling
- D. caching
Correct Answer: A 🗳️
Explanation: Only visible for Fast2test members. You can sign-up / login (it's free).
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