Microsoft DP-750 Exam Details & Actual Exam Questions

  • Exam Code/Number: DP-750
  • Exam Name/Title: Implementing Data Engineering Solutions Using Azure Databricks
  • Certification Provider: Microsoft
  • Corresponding Certification: Microsoft Certified: Fabric Data Engineer Associate
  • Exam Questions: 93
  • Updated On: Aug,31 2026
  • Certification Level: Professional

Microsoft Implementing Data Engineering Solutions Using Azure Databricks Exam Questions

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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
Related Certifications:Microsoft Certified: Azure Data Engineer Associate (DP-203)
Microsoft Certified: Fabric Data Engineer Associate (DP-700)
Available Languages:English
Exam Duration:100 minutes
Passing Score:700
Exam Format:Interactive items, Multiple choice, Case studies, Scenario-based questions
Recommended Training:DP-750 Training Course (DP-750T00)
Microsoft Learn DP-750 Study Guide
Exam Registration:Pearson VUE Exam Scheduling
Official Microsoft Certification Page
Sample Questions:Microsoft DP-750 Sample Questions
Exam Way:Online proctored exam via Pearson VUE
Pre Condition:Recommended experience with Azure Databricks, SQL, Python, and basic Azure services (Entra ID, Data Factory, Key Vault, Azure Storage).
Official Syllabus URL:https://learn.microsoft.com/en-us/credentials/certifications/implementing-data-engineering-solutions-using-azure-databricks/

Microsoft DP-750 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Prepare and process data30-35%- Data transformation and modeling
  • 1. SQL and PySpark transformations
    • 2. Delta Lake table design and SCD patterns
      • 3. Joins, aggregations, and normalization/denormalization
        - Data ingestion
        • 1. Batch ingestion using COPY INTO and CTAS
          • 2. Auto Loader and CDC ingestion patterns
            • 3. Streaming ingestion using Spark Structured Streaming
              - Data quality and validation
              • 1. Pipeline expectations and data quality constraints
                • 2. Handling nulls, duplicates, and missing data
                  • 3. Schema enforcement and validation rules
                    Topic 2: Deploy and manage data pipelines and workloads30-35%- Operational reliability
                    • 1. Monitoring and logging (Azure Monitor integration)
                      • 2. Error handling and retries
                        - Pipeline design and orchestration
                        • 1. Databricks Jobs and Workflows
                          • 2. Notebook-based vs declarative pipelines
                            - Lakehouse architecture operations
                            • 1. Delta Lake optimization and clustering strategies
                              • 2. Delta Live Tables pipelines
                                Topic 3: Configure and manage Azure Databricks environments15-20%- Workspace and compute configuration
                                • 1. Runtime, Spark, and Photon configuration
                                  • 2. Autoscaling, termination, and performance tuning
                                    • 3. Cluster types and configuration (job, all-purpose, serverless)
                                      - Security and authentication setup
                                      • 1. Access control for compute resources
                                        • 2. Azure Key Vault integration
                                          • 3. Service principals and managed identities
                                            Topic 4: Secure and govern data using Unity Catalog15-20%- Access control and policies
                                            • 1. Attribute-based access control (ABAC)
                                              • 2. Tags and policy enforcement
                                                • 3. Row-level and column-level security
                                                  - Data governance fundamentals
                                                  • 1. Data lineage and auditing
                                                    • 2. Catalog, schema, and table management


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