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Microsoft Implementing Data Engineering Solutions Using Azure Databricks : DP-750 Exam
- Exam Code: DP-750
- Exam Name: Implementing Data Engineering Solutions Using Azure Databricks
- Updated: Oct 27, 2026
- Q & A: 110 Questions and Answers
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Microsoft DP-750 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Deploy and maintain data pipelines and workloads | 30-35% | - Manage production workloads
|
| Topic 2: Set up and configure an Azure Databricks environment | 15-20% | - Create and configure Azure Databricks workspaces
|
| Topic 3: Prepare and process data | 30-35% | - Ingest and transform data
|
| Topic 4: Secure and govern Unity Catalog objects | 15-20% | - Implement governance and security
|
Microsoft Implementing Data Engineering Solutions Using Azure Databricks Sample Questions:
Question #1
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. caching
- D. spilling
Correct Answer: A 🗳️
Explanation: Only visible for RealExamFree members. You can sign-up / login (it's free).
Question #2
You have an Azure Databricks workspace that is enabled for Unity Catalog and contains a Delta table named Sales_orders. Sales.orders stores historical sales data.
You receive a daily CSV file daily that contains new sales records only. The file does NOT contain updates to existing rows You need to load the daily data into Sales.orders. The solution must meet the following requirements:
* Preserve the existing data.
* Add only the new records.
* Minimize processing effort.
Which command should include in the loading strategy?
- A. UPDATE
- B. INSERT OVERWRITE
- C. INSERT INTO
Correct Answer: C 🗳️
Explanation: Only visible for RealExamFree members. You can sign-up / login (it's free).
Question #3
You have an Azure Databricks workspace that contains the objects shown in the following table.
Name | Type
Catalog1 | Catalog
Schema1 | Schema
Sales1 | Table
Notebook1 | Notebook
Space1 | AI/BI Genie space
Users often use the following words to refer to a sale: transaction, event, order, and invoice.
You need to create a knowledge store. The solution must ensure that when the users use any of the words in Space1, Genie queries the Sales1 table. Any other Genie spaces must remain unaffected.
To which object should you add the instructions?
- A. Sales1
- B. Schema1
- C. Notebook1
- D. Space1
Correct Answer: D 🗳️
Explanation: Only visible for RealExamFree members. You can sign-up / login (it's free).
Question #4
You have an Azure Databricks workspace that is enabled for Unity Catalog and contains a managed Delta table named Table1.
Table1 stores customer profile data.
Business users must analyze how customer profile records change over time. They must also be able to query earlier versions of the table.
You need to implement a solution that:
Maintains persistent historical versions of customer profile records for long-term analysis.
Allows users to query earlier versions of the Delta table.
Minimizes maintenance effort.
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:
To record historical changes: Implement a Type 2 slowly changing dimension (SCD).
To support temporal analysis: Use Delta Lake time travel.
A Type 2 slowly changing dimension preserves customer-profile history by inserting a new record whenever a tracked attribute changes instead of overwriting the existing record. Effective dates, expiration dates, version values, or current-record indicators can identify which version applied during a particular period. This provides persistent business history for long-term analysis. Delta Lake time travel supports temporal analysis of the physical table by allowing users to query an earlier version with VERSION AS OF or TIMESTAMP AS OF. Time travel is useful for auditing and reproducing previous results, but its availability depends on retained Delta log entries and data files. Therefore, it should not replace a Type 2 SCD for permanent customer history. Together, the two features satisfy the historical-record and earlier-version requirements.
Question #5
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
Correct Answer: A 🗳️
Explanation: Only visible for RealExamFree members. You can sign-up / login (it's free).
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