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Microsoft Certified: Fabric Data Engineer Associate DP-750

DP-750

Exam Code: DP-750

Exam Name: Implementing Data Engineering Solutions Using Azure Databricks

Updated: Oct 17, 2026

Q&A Number: 93 Q&As

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Microsoft DP-750 Exam Syllabus Topics:

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

Microsoft Implementing Data Engineering Solutions Using Azure Databricks Sample Questions:

Question #1
You have an Azure Databricks workspace.
You have a streaming table named sales_order that is populated by using a Lakeflow Spark Declarative Pipelines (SDP) pipeline.
You need to create a new streaming table named sales_order_by_city that summarizes sales by city and calculates the total sales per city.
How should you complete the SQL statement? To answer, drag the appropriate values to the correct targets.
Each value may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.


Question #2
You have an Azure Databricks workspace
You are creating a Lakeflow Spark Declarative Pipelines (SDP) pipeline that scales automatically. You need to configure compute for the pipeline. The solution must minimize operational costs and effort. What should you use?

A. an all-purpose cluster that uses autoscaling
B. the existing SQL warehouse
C. a single-node, all-purpose cluster
D. a job cluster that uses autoscaling


Question #3
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. a task failure
D. job success


Question #4
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. Install the libraries by using pip3.
B. Install the libraries on Cluster1 and manually restart the cluster.
C. Create a custom dependency management script and run the script from a Databricks notebook.
D. Upload the libraries to Workspace1 and install the libraries on Cluster1.


Question #5
You have an Azure Databricks workspace that contains an all-purpose cluster named Cluster! You need to configure Cluster1 to meet the following requirements;
* The cluster must scale up automatically when workloads increase.
* The cluster must scale down automatically when workloads decrease.
The solution must minimize costs.
Which two actions should you perform? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.

A. Enable autoscaling for Cluster1.
B. Configure Cluster1 to terminate after 30 minutes of inactivity.
C. Specify a fixed number of workers.
D. Disable Photon acceleration.
E. Apply a compute policy that enables users to manage the cluster settings.


Solutions:

Question #1
Correct Answer: Only visible for members
Question #2
Correct Answer: D
Question #3
Correct Answer: B
Question #4
Correct Answer: A
Question #5
Correct Answer: A,B

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