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

DP-750 real exams

Exam Code: DP-750

Exam Name: Implementing Data Engineering Solutions Using Azure Databricks

Updated: Aug 27, 2026

Q & A: 93 Questions and Answers

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Microsoft DP-750 Exam Syllabus Topics:

SectionWeightObjectives
Secure and govern Unity Catalog objects15–20%- Manage data sharing and permissions
  • 1. Set up external locations and storage credentials
  • 2. Grant and revoke permissions, manage groups and service principals
- Implement data governance and security
  • 1. Manage catalogs, schemas, tables, views, and volumes
  • 2. Enforce data quality, lineage, and auditing
  • 3. Configure access control: row-level, column-level, attribute-based security
Set up and configure an Azure Databricks environment15–20%- 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
- Integrate with Azure services
  • 1. Connect to Azure Data Lake Storage, Azure Data Factory, Microsoft Entra ID
  • 2. Configure monitoring with Azure Monitor and diagnostic settings
Deploy and maintain data pipelines and workloads30–35%- Build and orchestrate pipelines
  • 1. Design and implement Lakeflow Spark Declarative Pipelines
  • 2. Implement CI/CD with Git, Databricks Asset Bundles, CLI, and APIs
  • 3. Configure Lakeflow Jobs: schedules, triggers, alerts, retries
- 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
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. Ingest batch and streaming data from multiple sources
  • 2. Transform using Spark SQL, PySpark, Scala, and Delta Lake
  • 3. Implement schema enforcement, schema drift, and slowly changing dimensions

Microsoft Implementing Data Engineering Solutions Using Azure Databricks Sample Questions:

1. You have an Azure Databricks workspace named Workspace1 that contains a lakehouse and is enabled for Unity Catalog.
You have a connection to a Microsoft SQL Server database named DB1.
You need to expose the schemas and tables of DB1 to meet the following requirements:
* The schemas and tables can be queried in Databricks.
* The schemas and tables appear alongside other Unity Catalog objects.
* The data is NOT copied into Databricks-managed storage.
Solution: You create a foreign catalog in Catalog Explorer.
Does this meet the goal?

A) Yes
B) No


2. 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.


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 by using pip3.
C) Install the libraries on Cluster1 and manually restart the cluster.
D) Create a custom dependency management script and run the script from a Databricks notebook.


4. 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.


5. You have an Azure Databricks workspace that is enabled for Unity Catalog.
You need to recommend a pipeline that ingests files from cloud storage, performs cleansing and enrichment transformations, and writes created Delta tables for analytics. The solution must minimize development effort and provide built-in monitoring and automatic retries.
What should you include in the recommendation?

A) a Lakeflow Spark Declarative Pipelines (SDPJ pipeline
B) an Azure Data Factory pipeline that uses data flows
C) a Databricks notebook triggered by a scheduled job
D) an Apache Spark Structured Streaming job


Solutions:

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

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