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Microsoft DP-750 Questions & Answers - in .pdf

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  • Total Q&A: 93
  • Update: Aug 29, 2026
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  • Vendor: Microsoft
  • Exam Code: DP-750
  • Exam Name: Implementing Data Engineering Solutions Using Azure Databricks
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Microsoft DP-750 Q&A - Testing Engine

DP-750 Study Guide
  • Total Q&A: 93
  • Update: Aug 29, 2026
  • Price: $59.99
Testing Engine
  • Vendor: Microsoft
  • Exam Code: DP-750
  • Exam Name: Implementing Data Engineering Solutions Using Azure Databricks
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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
Available Languages:English
Exam Duration:100 minutes
Passing Score:700
Exam Format:Multiple choice, Case studies, Scenario-based questions, Interactive items
Related Certifications:Microsoft Certified: Azure Data Engineer Associate (DP-203)
Microsoft Certified: Fabric Data Engineer Associate (DP-700)
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: Secure and govern data using Unity Catalog15-20%- Access control and policies
  • 1. Tags and policy enforcement
    • 2. Row-level and column-level security
      • 3. Attribute-based access control (ABAC)
        - Data governance fundamentals
        • 1. Data lineage and auditing
          • 2. Catalog, schema, and table management
            Topic 2: Configure and manage Azure Databricks environments15-20%- Workspace and compute configuration
            • 1. Autoscaling, termination, and performance tuning
              • 2. Cluster types and configuration (job, all-purpose, serverless)
                • 3. Runtime, Spark, and Photon configuration
                  - Security and authentication setup
                  • 1. Access control for compute resources
                    • 2. Service principals and managed identities
                      • 3. Azure Key Vault integration
                        Topic 3: Prepare and process data30-35%- Data quality and validation
                        • 1. Handling nulls, duplicates, and missing data
                          • 2. Schema enforcement and validation rules
                            • 3. Pipeline expectations and data quality constraints
                              - Data ingestion
                              • 1. Batch ingestion using COPY INTO and CTAS
                                • 2. Streaming ingestion using Spark Structured Streaming
                                  • 3. Auto Loader and CDC ingestion patterns
                                    - Data transformation and modeling
                                    • 1. SQL and PySpark transformations
                                      • 2. Delta Lake table design and SCD patterns
                                        • 3. Joins, aggregations, and normalization/denormalization
                                          Topic 4: Deploy and manage data pipelines and workloads30-35%- Lakehouse architecture operations
                                          • 1. Delta Lake optimization and clustering strategies
                                            • 2. Delta Live Tables pipelines
                                              - Operational reliability
                                              • 1. Error handling and retries
                                                • 2. Monitoring and logging (Azure Monitor integration)
                                                  - Pipeline design and orchestration
                                                  • 1. Notebook-based vs declarative pipelines
                                                    • 2. Databricks Jobs and Workflows

                                                      Microsoft Implementing Data Engineering Solutions Using Azure Databricks Sample Questions:

                                                      Question 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-fillna(0, subset=[ ' order_amount ' ])
                                                      Does this meet the goal?

                                                      A. No
                                                      B. Yes


                                                      Question 2

                                                      You have an Azure Databricks workspace named Workspace1 that is attached to a Unity Catalog metastore named metastore1 You need to register an Azure Storage account named account1 that has a hierarchical namespace enabled as an external location The external location must use a managed identity to authenticate to account1 and the solution must follow the principle of least privilege.
                                                      Which three actions should you perform in sequence ' To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.


                                                      Question 3

                                                      You have an Azure Databricks workspace that is enabled for Unity Catalog.
                                                      You need to create an external volume named Volume1 in an existing schema. Volume1 must expose files from an Azure Storage container. The solution must meet the following requirements:
                                                      * Ensure that authentication does NOT require storing credentials in Databricks
                                                      * Ensure that users can access the files, but NOT modify the files.
                                                      * Follow the principle of least privilege
                                                      Which type of authentication should you configure, and which permission should you grant to the users? To answer, select the appropriate options in the answer area.
                                                      NOTE: Each correct selection is worth one point.


                                                      Question 4

                                                      You have an Azure Databricks workspace that contains a job in Lakeflow Jobs named Job1. Job1 contains multiple tasks.
                                                      Failures of non-critical tasks must be logged but must NOT trigger notifications. Notifications must be triggered only when critical tasks have failed, and Job1 has completed You need to configure the job alerting behavior.
                                                      What should trigger a notification?

                                                      A. a task failure
                                                      B. job success
                                                      C. a job failure
                                                      D. task success


                                                      Question 5

                                                      You need to curate Unity Catalog objects that reference the ERP data. The solution must meet the governance requirements.
                                                      What should you do?

                                                      A. Run the ALTER TABLE command on the foreign catalog tables to add new columns that are required for analytics.
                                                      B. In the managed analytics catalog, create views that reference foreign catalog tables that use three-level naming.
                                                      C. Create Delta tables directly inside the foreign catalog by running the CREATE TABLE AS SELECT (CTAS) command.
                                                      D. Create a volume in the foreign catalog and store curated Delta tables in the volume.


                                                      Solutions:

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

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                                                      DP-750 Related Exams
                                                      DP-700-KR - Implementing Data Engineering Solutions Using Microsoft Fabric (DP-700 Korean Version)
                                                      DP-700J - Implementing Data Engineering Solutions Using Microsoft Fabric (DP-700日本語版)
                                                      DP-700 - Implementing Data Engineering Solutions Using Microsoft Fabric
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