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Updated: Oct 18, 2026
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Microsoft DP-750 Exam Overview:
| Certification Vendor: | Microsoft |
|---|---|
| Exam Name: | Implementing Data Engineering Solutions Using Azure Databricks |
| Exam Number: | DP-750 |
| Exam Format: | Multiple choice, Case studies, Multiple select, Scenario-based questions |
| Exam Price: | 165 USD |
| Available Languages: | Korean, English, Spanish, French, Japanese, Portuguese (Brazil), Chinese (Simplified), German |
| Real Exam Qty: | 40–60 |
| Exam Duration: | 100 minutes |
| Certificate Validity Period: | 1 year |
| Passing Score: | 700 |
| Related Certifications: | Microsoft Certified: Azure Developer Associate Microsoft Certified: Azure Data Engineer Associate |
| Recommended Training: | Microsoft Learn Learning Paths Course DP-750T00-A: Implementing Data Engineering Solutions Using Azure Databricks |
| Exam Registration: | Microsoft Learn Pearson VUE |
| Sample Questions: | Microsoft DP-750 Sample Questions |
| Exam Way: | Online proctored or onsite testing center |
| Pre Condition: | No mandatory prerequisites; recommended experience: data integration, modeling, pipelines, SQL, Python, Azure services, Git |
| Official Syllabus URL: | https://learn.microsoft.com/en-us/credentials/certifications/resources/study-guides/dp-750 |
Microsoft DP-750 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Secure and govern Unity Catalog objects | 15–20% | - Manage data sharing and permissions
|
| Topic 2: Set up and configure an Azure Databricks environment | 15–20% | - Integrate with Azure services
|
| Topic 3: Deploy and maintain data pipelines and workloads | 30–35% | - Build and orchestrate pipelines
|
| Topic 4: Prepare and process data | 30–35% | - Ingest and transform data
|
Microsoft Implementing Data Engineering Solutions Using Azure Databricks Sample Questions:
You have an Azure Databricks workspace.
You need to ingest streaming data from Azure Event Hubs by using Apache Spark Structured Streaming The solution must authenticate to Event Hubs and read the event payload.
How should you complete the PySpark code segment? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Correct Answer:
Explanation:
Reading from Azure Event Hubs in Spark Structured Streaming requires three things:
An EventHubsConf object built with the Event Hubs connection string (eventhubs.connectionString). This object is then converted to a map with .toMap before being passed to Spark.
spark.readStream.format( ' eventhubs ' ).options(**ehConf).load() to create the streaming DataFrame. The ' eventhubs ' format is provided by the azure-eventhubs-spark connector library.
A cast( ' string ' ) on the body column to decode the binary payload. Event Hubs delivers messages with the raw event bytes in a column called body - without the cast, you get binary data rather than the readable JSON or text payload.
This is the standard, documented integration pattern for connecting Azure Databricks to Event Hubs with Structured Streaming, providing the checkpoint-based exactly-once semantics required by the Contoso telemetry pipeline.
Reference: https://learn.microsoft.com/en-us/azure/databricks/connect/storage/events/eventhubs
You need to recommend a compute type for the production ingestion workloads and BI workloads. The solution must meet the environment and compute requirements.
What should you recommend for each type of workload? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Correct Answer:
Explanation:
Production ingestion: Job compute
BI: Serverless SQL warehouse
Job compute is designed for automated production workloads executed through Lakeflow Jobs. Its lifecycle can be tied to the job run, providing workload isolation and avoiding the cost of maintaining an interactive all- purpose cluster continuously. It is therefore appropriate for scheduled ingestion and transformation processing. A serverless SQL warehouse is designed for BI and Databricks SQL workloads. It provides rapid startup, automatic infrastructure management, scaling, and optimized SQL-query execution for dashboards and reporting tools. All-purpose compute is intended primarily for interactive notebook development and exploration, while shared compute does not provide the same job-specific isolation or SQL-serving experience. Consequently, job compute should support production ingestion, and a serverless SQL warehouse should serve the BI workload.
You have an Azure Databricks workspace.
You have an Apache Spark Structured Streaming job named Job! that processes data continuously and fails periodically due to transient errors You need to ensure that Job! meets the following requirements
* Resumes processing from the point that Job1 failed
* Minimizes how long it takes to restart Job!
* Minimizes the costs to restart Job!
What should you do?
- A. Implement checkpointing.
- B. Decrease the retry interval.
- C. Increase the minimum number of nodes in the cluster
- D. Add an alert and manually restart Job1.
Correct Answer: A 🗳️
Explanation: Only visible for Prep4King members. You can sign-up / login (it's free).
You have an Azure Databricks workspace that is enabled for Unity Catalog You have a complex job named Job1 that contains eight tasks. Job! takes multiple hours to complete During the last job run, the final task fails due to a transient issue.
You need to retry the last task without rerunning tasks that have already completed.
What should you do?
- A. Restart Job!
- B. Repair the current job run.
- C. Disable and reenable the job schedule.
- D. Update the job parameters.
Correct Answer: B 🗳️
Explanation: Only visible for Prep4King members. You can sign-up / login (it's free).
You have an Azure Databricks workspace named Workspace! that uses a Git repository. The repository contains a Databricks notebook named Notebook1.
From the main branch, you create a feature branch named Branch! and commit changes to Notebooks Another user commits changes to Notebook1 in main.
When you attempt to merge Branch! into main, the merge fails due to conflicts.
You need to merge Branch! into the main branch. The solution must ensure that Notebook1 includes all the changes from both the branches.
What should you do?
- A. Apply the main branch changes to Branch! and resolve the conflicts.
- B. From Workspace1, clone Branch! as a new repository.
- C. Apply the changes directly to the main branch.
- D. From Workspace1, clone the mam branch as a new repository.
Correct Answer: A 🗳️
Explanation: Only visible for Prep4King members. You can sign-up / login (it's free).
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