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

  • Exam Code: DP-750
  • Exam Name: Implementing Data Engineering Solutions Using Azure Databricks
  • Updated: Jul 30, 2026
  • Q & A: 93 Questions and Answers

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Microsoft DP-750 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Prepare and process data30-35%- Ingest and transform data
  • 1. Optimize storage and table performance
  • 2. Model and partition data
  • 3. Use Auto Loader and batch ingestion
  • 4. Transform data using SQL and Python
  • 5. Implement Delta Lake tables
  • 6. Implement streaming data processing
  • 7. Implement data quality controls
  • 8. Apply medallion architecture patterns
Topic 2: Set up and configure an Azure Databricks environment15-20%- Create and configure Azure Databricks workspaces
  • 1. Configure workspace settings
  • 2. Configure compute resources and clusters
  • 3. Manage Databricks runtimes
  • 4. Configure networking and connectivity
Topic 3: Secure and govern Unity Catalog objects15-20%- Implement governance and security
  • 1. Implement access control and permissions
  • 2. Implement data-sharing capabilities
  • 3. Manage data lineage and auditing
  • 4. Manage catalogs, schemas, and tables
  • 5. Configure Unity Catalog
Topic 4: Deploy and maintain data pipelines and workloads30-35%- Manage production workloads
  • 1. Deploy workloads using Databricks Asset Bundles
  • 2. Implement CI/CD processes
  • 3. Create and manage Lakeflow Jobs
  • 4. Maintain production data engineering solutions
  • 5. Integrate Git-based development workflows
  • 6. Monitor and troubleshoot pipelines
  • 7. Optimize workload performance and reliability

Microsoft Implementing Data Engineering Solutions Using Azure Databricks Sample Questions:

1. 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


2. 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) caching
B) shuffling
C) skewing
D) spilling


3. 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 foreign catalog in Catalog Explorer.
Does this meet the goal?

A) Yes
B) No


4. 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) Notebook1
C) Schema1
D) Space1


5. 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 Lakeflow Connect pipeline and connect it to DB1. Does this meet the goal?

A) Yes
B) No


Solutions:

Question # 1
Answer: A
Question # 2
Answer: C
Question # 3
Answer: A
Question # 4
Answer: D
Question # 5
Answer: B

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