a lot of the same questions but there are some differences. Still valid. Tested out today in U.S. and was extremely prepared, did not even come close to failing.
Jesse 
DP-750 Q&A - in .pdf
- Exam Code: DP-750
- Exam Name: Implementing Data Engineering Solutions Using Azure Databricks
- Updated: Oct 16, 2026
- Q & A: 93 Questions and Answers
- Convenient, easy to study.
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- Updated: Oct 16, 2026
- Q & A: 93 Questions and Answers
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DP-750 Q&A - Testing Engine
- Exam Code: DP-750
- Exam Name: Implementing Data Engineering Solutions Using Azure Databricks
- Updated: Oct 16, 2026
- Q & A: 93 Questions and Answers
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Microsoft DP-750 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Secure and govern data using Unity Catalog | 15-20% | - Data governance fundamentals
|
| Topic 2: Deploy and manage data pipelines and workloads | 30-35% | - Lakehouse architecture operations
|
| Topic 3: Prepare and process data | 30-35% | - Data quality and validation
|
| Topic 4: Configure and manage Azure Databricks environments | 15-20% | - Security and authentication setup
|
Microsoft Implementing Data Engineering Solutions Using Azure Databricks Sample Questions:
Question #1
You have an Azure Databricks workspace and a remote Git repository named Repo1. Repo1 contains two branches named main and Branch1.
You are on a development team that works in Repo1.
You commit changes to Branch1 and must merge the changes into main.
Before completing the merge, you need to meet the following requirements:
* Ensure that Branch1 includes the changes committed to main since Branch1 was created.
* Ensure that merge conflicts are detected and resolved.
What should you do first?
- A. Merge main into Branch1.
- B. Create a pull request for Branch1.
- C. Pull the latest changes from main.
- D. Pull the latest changes from Branch1.
Correct Answer: C 🗳️
Explanation: Only visible for PracticeDump members. You can sign-up / login (it's free).
Question #2
You have an Azure Databricks workspace that is enabled for Unity Catalog.
You have a Lakeflow Spark Declarative Pipelines (SDP) pipeline that writes records to a Delta table named Table1 by using a data quality rule named rule1 You need to meet the following requirements:
* Records that violate rule! must NOT be written to Table1. but the pipeline must continue processing valid records.
* Data engineers must be able to review expectation metrics by using minimal development 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:
Two things are needed here:
For the rule enforcement: use @dlt.expect_or_drop. This drops any record that violates rule1 before it reaches Table1, while the pipeline continues processing all valid records. The table only ever receives clean data.
For reviewing metrics: the Lakeflow SDP Pipeline UI is the right tool - zero development effort required.
The pipeline graph shows expectation pass/fail counts directly on each table node, and the event log provides a detailed per-batch breakdown of how many records were dropped and why. Data engineers can inspect this at any time without writing additional monitoring queries or connecting external dashboards.
This combination is one of the strongest arguments for SDP over hand-coded Structured Streaming:
expectation observability is built in, not bolted on.
Reference: https://learn.microsoft.com/en-us/azure/databricks/delta-live-tables/expectations
Question #3
You have an Azure Databricks workspace that is enabled for Unity Catalog and contains a managed Delta table named Table1. Table1 stores customer data.
You need to implement a data retention solution that meets the following requirements:
Deleted data must be retained for 30 days to support audits.
Deleted data that is older than 30 days must be removed permanently.
The solution must minimize administrative effort.
Which two properties should you configure? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
- A. delta.enableDeletionVectors
- B. delta.autoOptimize.autoCompact
- C. delta.deletedFileRetentionDuration
- D. delta.logRetentionDuration
- E. delta.timeUntilArchived
Correct Answer: C,D 🗳️
Explanation: Only visible for PracticeDump members. You can sign-up / login (it's free).
Question #4
You have an Azure Databricks workspace that contains a Git folder and uses Azure Repos as the Git provider.
From the main branch, you create a branch named Branch1. You commit changes to Branch1.
You need to incorporate the changes from Branch1 into main The solution must preserve the commit history in the repository. Which command should you run?
- A. merge
- B. rebase
- C. pull
- D. Push
Correct Answer: A 🗳️
Explanation: Only visible for PracticeDump members. You can sign-up / login (it's free).
Question #5
You need to deploy Declarative Automation Bundles to a development environment. The solution must support automated and repeatable deployments across environments.
What should you use?
- A. the Jobs UI
- B. the Databricks SDK for Python
- C. Git folders
- D. the Databricks CLI
Correct Answer: D 🗳️
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