Microsoft DP-750 Exam : Implementing Data Engineering Solutions Using Azure Databricks

  • Exam Code: DP-750
  • Exam Name: Implementing Data Engineering Solutions Using Azure Databricks
  • Updated: Aug 01, 2026
  • Q & A: 93 Questions and Answers

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Microsoft DP-750 Exam Syllabus Topics:

SectionWeightObjectives
Prepare and process data30–35%- Optimize and manage data storage
  • 1. Handle structured, semi-structured, and unstructured data
  • 2. Optimize Delta tables: partitioning, Z-ordering, vacuum, optimize
  • 3. Implement lakehouse architecture and manage table versions
- Ingest and transform data
  • 1. Transform using Spark SQL, PySpark, Scala, and Delta Lake
  • 2. Ingest batch and streaming data from multiple sources
  • 3. Implement schema enforcement, schema drift, and slowly changing dimensions
Deploy and maintain data pipelines and workloads30–35%- Monitor, troubleshoot, and maintain workloads
  • 1. Troubleshoot failures, repair and restart jobs
  • 2. Apply SDLC practices and version control
  • 3. Monitor performance, logs, and execution metrics
- Build and orchestrate pipelines
  • 1. Design and implement Lakeflow Spark Declarative Pipelines
  • 2. Implement CI/CD with Git, Databricks Asset Bundles, CLI, and APIs
  • 3. Configure Lakeflow Jobs: schedules, triggers, alerts, retries
Set up and configure an Azure Databricks environment15–20%- Integrate with Azure services
  • 1. Configure monitoring with Azure Monitor and diagnostic settings
  • 2. Connect to Azure Data Lake Storage, Azure Data Factory, Microsoft Entra ID
- Select and configure compute resources
  • 1. Manage workspace settings, permissions, and networking
  • 2. Choose compute types: serverless, job compute, SQL warehouse, classic compute
  • 3. Configure cluster policies, instance pools, and libraries
Secure and govern Unity Catalog objects15–20%- Manage data sharing and permissions
  • 1. Grant and revoke permissions, manage groups and service principals
  • 2. Set up external locations and storage credentials
- Implement data governance and security
  • 1. Enforce data quality, lineage, and auditing
  • 2. Manage catalogs, schemas, tables, views, and volumes
  • 3. Configure access control: row-level, column-level, attribute-based security

Microsoft Implementing Data Engineering Solutions Using Azure Databricks Sample Questions:

1. You have an Azure Databricks workspace named Workspace1 that contains a lakehouse 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 Databricks access connector.
Does this meet the goal?

A) Yes
B) No


2. 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.


3. 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) Repair the current job run.
B) Restart Job!
C) Update the job parameters.
D) Disable and reenable the job schedule.


4. You have an Azure Databticks workspace that contains an all-purpose compute cluster named Cluster1.
Cluser1 is used for
interactive development.
You need to configure Cluster1 to meet the following requirements:
* Automatically add and remove worker nodes based on workload demand
* Automatically shut down when the cluster has been idle for a specific period.
What should you configure for each requirement? To answer, drag the appropriate options to the correct requirements. Each option 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.


5. 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 the mam branch as a new repository.
C) Apply the changes directly to the main branch.
D) From Workspace1, clone Branch! as a new repository.


Solutions:

Question # 1
Answer: B
Question # 2
Answer: Only visible for members
Question # 3
Answer: A
Question # 4
Answer: Only visible for members
Question # 5
Answer: A

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