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

  • Exam Code: DP-750
  • Exam Name: Implementing Data Engineering Solutions Using Azure Databricks
  • Updated: Sep 29, 2026
  • Q&As: 93 Questions and Answers

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Microsoft DP-750 Exam Syllabus Topics:

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

                                                      Microsoft Implementing Data Engineering Solutions Using Azure Databricks Sample Questions:

                                                      Question #1

                                                      You have an Azure Databricks workspace.
                                                      You have an Apache Spark Structured Streaming job named Job! that processes data continuously and fails periodically due to transient errors You need to ensure that Job! meets the following requirements
                                                      * Resumes processing from the point that Job1 failed
                                                      * Minimizes how long it takes to restart Job!
                                                      * Minimizes the costs to restart Job!
                                                      What should you do?

                                                      • A. Decrease the retry interval.
                                                      • B. Implement checkpointing.
                                                      • C. Add an alert and manually restart Job1.
                                                      • D. Increase the minimum number of nodes in the cluster
                                                      Reveal Solution  Discussion  0

                                                      Correct Answer: B  🗳️

                                                      Explanation: Only visible for TestkingPass members. You can sign-up / login (it's free).

                                                      Question #2

                                                      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. task success
                                                      • B. a job failure
                                                      • C. job success
                                                      • D. a task failure
                                                      Reveal Solution  Discussion  0

                                                      Correct Answer: B  🗳️

                                                      Explanation: Only visible for TestkingPass members. You can sign-up / login (it's free).

                                                      Question #3

                                                      You have an Azure Databricks workspace named Workspace1. You create a compute cluster named Cluser1 that will be used to ingest data.
                                                      You need to install the required libraries on Cluster 1. The solution must use Unity Catalog for access control.
                                                      What should you do?

                                                      • A. Upload the libraries to Workspace1 and install the libraries on Cluster1.
                                                      • B. Install the libraries on Cluster1 and manually restart the cluster.
                                                      • C. Install the libraries by using pip3.
                                                      • D. Create a custom dependency management script and run the script from a Databricks notebook.
                                                      Reveal Solution  Discussion  0

                                                      Correct Answer: C  🗳️

                                                      Explanation: Only visible for TestkingPass members. You can sign-up / login (it's free).

                                                      Question #4

                                                      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. skewing
                                                      • B. shuffling
                                                      • C. caching
                                                      • D. spilling
                                                      Reveal Solution  Discussion  0

                                                      Correct Answer: A  🗳️

                                                      Question #5

                                                      You have an Azure Databricks workspace that uses serverless compute.
                                                      You need to ingest data by using Lakeflow Jobs. New records must be processed as soon as they become available.
                                                      Which type of job trigger should you use for the ingestion?

                                                      • A. file arrival
                                                      • B. manual
                                                      • C. continuous
                                                      • D. scheduled
                                                      Reveal Solution  Discussion  0

                                                      Correct Answer: C  🗳️

                                                      Explanation: Only visible for TestkingPass members. You can sign-up / login (it's free).

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