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

  • Exam Code: DP-750
  • Exam Name: Implementing Data Engineering Solutions Using Azure Databricks
  • Updated: Aug 02, 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. Configure access control: row-level, column-level, attribute-based security
  • 2. Enforce data quality, lineage, and auditing
  • 3. Manage catalogs, schemas, tables, views, and volumes
Set up and configure an Azure Databricks environment15–20%- Select and configure compute resources
  • 1. Choose compute types: serverless, job compute, SQL warehouse, classic compute
  • 2. Manage workspace settings, permissions, and networking
  • 3. Configure cluster policies, instance pools, and libraries
- 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
Deploy and maintain data pipelines and workloads30–35%- 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
- Build and orchestrate pipelines
  • 1. Implement CI/CD with Git, Databricks Asset Bundles, CLI, and APIs
  • 2. Configure Lakeflow Jobs: schedules, triggers, alerts, retries
  • 3. Design and implement Lakeflow Spark Declarative Pipelines
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. Implement schema enforcement, schema drift, and slowly changing dimensions
  • 2. Ingest batch and streaming data from multiple sources
  • 3. Transform using Spark SQL, PySpark, Scala, and Delta Lake

Microsoft Implementing Data Engineering Solutions Using Azure Databricks Sample Questions:

1. You have an Azure Databricks workspace that is enabled for Unity Catalog and contains two managed Delta tables named sales.schema1.table1 and sales.schema1.table2.
sales.schema1.table1 contains sales data from the current year.
sales.schema1.table2 contains historical data.
You need to load all the rows from sales.schema1.table1 into sales.schema1.table2. The solution must preserve any existing data in sales.schema1.table2 and minimize processing effort.
Which command should you run?

A) INSERT INTO sales.schema1.table2 SELECT * FROM sales.schema1.table1;
B) INSERT OVERWRITE sales.schema1.table2 SELECT * FROM sales.schema1.table1;
C) CREATE OR REPLACE TABLE sales.schema1.table2 AS SELECT * FROM sales.schema1.table1;
D) CREATE TABLE sales.schema1.table2 AS SELECT * FROM sales.schema1.table1;


2. You have an Azure Databricks workspace that contains a job in Lakeflow Jobs named Job1.
Job! contains three tasks named Task1, Task2. and Task3.
If Task1 fails, Task2 and Task3 must be prevented from running. Successfully completed tasks must NOT rerun during recovery.
You need to configure Job1 to support controlled failure handling and recovery What should you configure? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.


3. You have an Azure Databricks workspace.
You need to ingest streaming data from Azure Event Hubs by using Apache Spark Structured Streaming The solution must authenticate to Event Hubs and read the event payload.
How should you complete the PySpark code segment? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.


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 have a Lakeflow Spark Declarative Pipelines (SDP) pipeline that writes records to a Delta table named Table1 by using a data quality rule named rule1 You need to meet the following requirements:
* Records that violate rule! must NOT be written to Table1. but the pipeline must continue processing valid records.
* Data engineers must be able to review expectation metrics by using minimal development effort.
What should you do? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.


Solutions:

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