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Updated: Aug 03, 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 Duration:100 minutes
Certificate Validity Period:1 year
Passing Score:700
Exam Format:Multiple choice, Multiple select, Case studies, Scenario-based questions
Related Certifications:Microsoft Certified: Azure Data Engineer Associate
Microsoft Certified: Azure Developer Associate
Real Exam Qty:40–60
Exam Price:165 USD
Available Languages:English, Japanese, Chinese (Simplified), Korean, German, French, Spanish, Portuguese (Brazil)
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:

SectionWeightObjectives
Topic 1: Prepare and process data30–35%- Ingest and transform data
  • 1. Implement schema enforcement, schema drift, and slowly changing dimensions
  • 2. Transform using Spark SQL, PySpark, Scala, and Delta Lake
  • 3. Ingest batch and streaming data from multiple sources
- Optimize and manage data storage
  • 1. Optimize Delta tables: partitioning, Z-ordering, vacuum, optimize
  • 2. Handle structured, semi-structured, and unstructured data
  • 3. Implement lakehouse architecture and manage table versions
Topic 2: Deploy and maintain data pipelines and workloads30–35%- Monitor, troubleshoot, and maintain workloads
  • 1. Monitor performance, logs, and execution metrics
  • 2. Apply SDLC practices and version control
  • 3. Troubleshoot failures, repair and restart jobs
- Build and orchestrate pipelines
  • 1. Configure Lakeflow Jobs: schedules, triggers, alerts, retries
  • 2. Design and implement Lakeflow Spark Declarative Pipelines
  • 3. Implement CI/CD with Git, Databricks Asset Bundles, CLI, and APIs
Topic 3: 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. Configure cluster policies, instance pools, and libraries
  • 2. Choose compute types: serverless, job compute, SQL warehouse, classic compute
  • 3. Manage workspace settings, permissions, and networking
Topic 4: 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. Configure access control: row-level, column-level, attribute-based security
  • 3. Manage catalogs, schemas, tables, views, and volumes

Microsoft Implementing Data Engineering Solutions Using Azure Databricks Sample Questions:

1. 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 CLI
C) the Databricks SDK for Python
D) Git folders


2. You have an Azure Databricks account that contains a single workspace named Workspace1. Workspace1 is enabled for Unity Catalog.
You discover that data access events for Unity Catalog tables fail to appear in the logs.
You need to ensure that all the data access events are captured centrally for auditing purposes. The log data must be available for analysis as quickly as possible.
What should you do? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.


3. You need to deploy Databricks Asset Bundles to a development environment. The solution must support automated and repeatable deployments across environments.
What should you use?

A) the Azure Developer CLI (azd)
B) the Databricks CLI
C) the Azure Command-Line Interface (CLI)
D) Git folders


4. You need to configure resiliency for a job in Lakeflow Jobs named Job1 to meet the pipeline deployment and operation requirements.
What should you do?

A) Enable task-level retries on the ingestion task and leave downstream tasks unchanged.
B) Enable a restart upon a job failure and set the retry count to 0.
C) Configure Job1 to start always from the first task when any task fails.
D) Disable retries and configure Job1 to run manually only.


5. You have an Azure Databricks workspace that is enabled for Unity Catalog.
You need to implement a daily batch data process that requires complex and highly customized Python transformations. The solution must minimize additional complexity.
What should you include in the solution?

A) a continuous job in Lakeflow Jobs
B) an Azure Data Factory pipeline that has data flows
C) a Databricks notebook triggered by a job
D) a Lakeflow Spark Declarative Pipelines (SDP) pipeline


Solutions:

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

Microsoft DP-750 Exam Overview:

Certification Vendor:Microsoft
Exam Name:Implementing Data Engineering Solutions Using Azure Databricks
Exam Number:DP-750
Exam Duration:100 minutes
Certificate Validity Period:1 year
Passing Score:700
Exam Format:Multiple choice, Multiple select, Case studies, Scenario-based questions
Related Certifications:Microsoft Certified: Azure Data Engineer Associate
Microsoft Certified: Azure Developer Associate
Real Exam Qty:40–60
Exam Price:165 USD
Available Languages:English, Japanese, Chinese (Simplified), Korean, German, French, Spanish, Portuguese (Brazil)
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:

SectionWeightObjectives
Topic 1: Prepare and process data30–35%- Ingest and transform data
  • 1. Implement schema enforcement, schema drift, and slowly changing dimensions
  • 2. Transform using Spark SQL, PySpark, Scala, and Delta Lake
  • 3. Ingest batch and streaming data from multiple sources
- Optimize and manage data storage
  • 1. Optimize Delta tables: partitioning, Z-ordering, vacuum, optimize
  • 2. Handle structured, semi-structured, and unstructured data
  • 3. Implement lakehouse architecture and manage table versions
Topic 2: Deploy and maintain data pipelines and workloads30–35%- Monitor, troubleshoot, and maintain workloads
  • 1. Monitor performance, logs, and execution metrics
  • 2. Apply SDLC practices and version control
  • 3. Troubleshoot failures, repair and restart jobs
- Build and orchestrate pipelines
  • 1. Configure Lakeflow Jobs: schedules, triggers, alerts, retries
  • 2. Design and implement Lakeflow Spark Declarative Pipelines
  • 3. Implement CI/CD with Git, Databricks Asset Bundles, CLI, and APIs
Topic 3: 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. Configure cluster policies, instance pools, and libraries
  • 2. Choose compute types: serverless, job compute, SQL warehouse, classic compute
  • 3. Manage workspace settings, permissions, and networking
Topic 4: 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. Configure access control: row-level, column-level, attribute-based security
  • 3. Manage catalogs, schemas, tables, views, and volumes

Microsoft Implementing Data Engineering Solutions Using Azure Databricks Sample Questions:

1. 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 CLI
C) the Databricks SDK for Python
D) Git folders


2. You have an Azure Databricks account that contains a single workspace named Workspace1. Workspace1 is enabled for Unity Catalog.
You discover that data access events for Unity Catalog tables fail to appear in the logs.
You need to ensure that all the data access events are captured centrally for auditing purposes. The log data must be available for analysis as quickly as possible.
What should you do? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.


3. You need to deploy Databricks Asset Bundles to a development environment. The solution must support automated and repeatable deployments across environments.
What should you use?

A) the Azure Developer CLI (azd)
B) the Databricks CLI
C) the Azure Command-Line Interface (CLI)
D) Git folders


4. You need to configure resiliency for a job in Lakeflow Jobs named Job1 to meet the pipeline deployment and operation requirements.
What should you do?

A) Enable task-level retries on the ingestion task and leave downstream tasks unchanged.
B) Enable a restart upon a job failure and set the retry count to 0.
C) Configure Job1 to start always from the first task when any task fails.
D) Disable retries and configure Job1 to run manually only.


5. You have an Azure Databricks workspace that is enabled for Unity Catalog.
You need to implement a daily batch data process that requires complex and highly customized Python transformations. The solution must minimize additional complexity.
What should you include in the solution?

A) a continuous job in Lakeflow Jobs
B) an Azure Data Factory pipeline that has data flows
C) a Databricks notebook triggered by a job
D) a Lakeflow Spark Declarative Pipelines (SDP) pipeline


Solutions:

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

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