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Microsoft DP-750 : Implementing Data Engineering Solutions Using Azure Databricks
Exam Code: DP-750
Exam Name: Implementing Data Engineering Solutions Using Azure Databricks
Updated: Sep 11, 2026
Q & A: 93 Questions and Answers
DP-750 Free Demo download
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Microsoft DP-750 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Deploy and manage data pipelines and workloads | 30-35% | - Lakehouse architecture operations
|
| Topic 2: Prepare and process data | 30-35% | - Data quality and validation
|
| Topic 3: Configure and manage Azure Databricks environments | 15-20% | - Security and authentication setup
|
| Topic 4: Secure and govern data using Unity Catalog | 15-20% | - Access control and policies
|
Microsoft Implementing Data Engineering Solutions Using Azure Databricks Sample Questions:
Question #1
You manage Declarative Automation Bundles by using the Databricks CLI.
You run the following command in a terminal window.
databricks bundle init
What occurs when you run the command?
- A. A new Declarative Automation Bundles project structure is created.
- B. Jobs or pipelines defined in the bundle are run.
- C. Bundle-defined resources are deployed to the Databricks workspace.
- D. The bundle configuration is validated.
Answer: A
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Question #2
You have an Azure Databricks workspace named Workspace1 that contains a takehouse and is enabled for Unity Catalog.
You have a connection to a Microsoft SQL Server database named DB1.
You need to expose the schemas and tables of DB1 to meet the following requirements:
* The schemas and tables can be queried in Databricks.
* The schemas and tables appear alongside other Unity Catalog objects.
* The data is NOT copied into Databricks-managed storage.
Solution: You create a new native catalog in Unity Catalog. Does this meet the goal?
- A. Yes
- B. No
Answer: B
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Question #3
You have an Azure Databricks workspace that is enabled for Unity Catalog and contains a managed Delta table named Sales. Sales stores transaction data and contains the following columns:
* transactionjd (string)
* transaction date (date)
* amount (decimal)
You need to implement the following data quality requirements by using table-level data quality enforcement:
* amount must be greater than 0.
* transaction id must never be null.
* Invalid records must be rejected when data is written to the Sales table.
What should you do?
- A. Configure row-level security (RLS) where transactionjd is null or amount is less than or equal to 0.
- B. Use a select statement with where conditions to validate the data before querying.
- C. Create a view that filters out rows where transactionjd is null or amount is less than or equal to 0.
- D. Add a not null constraint to transactionjd and a check constraint to amount.
Answer: D
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Question #4
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. From Workspace1, clone Branch1 as a new Git folder.
- B. Apply the changes directly to the main branch.
- C. Merge main into Branch1, resolve the conflicts, and then complete the merge of Branch1 into main.
- D. From Workspace1, clone the main branch as a new Git folder.
Answer: C
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Question #5
You have an Azure Databricks workspace that is enabled for Unity Catalog.
You plan to create a job in Lakeflow Jobs named Job1 that:
* Ingests data from cloud storage
* Runs two independent transformation tasks
The transformation tasks must run only after the ingestion completes and must run in parallel.
You need to design the task logic for Job1.
What should you configure?
- A. independent tasks with no defined dependencies
- B. a single task that performs ingestion and transformations sequentially
- C. two ingestion tasks, each followed by a transformation task
- D. One ingestion task with two parallel downstream transformation tasks
Answer: D
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