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

Exam Code: DP-750
Exam Name: Implementing Data Engineering Solutions Using Azure Databricks
Updated: Jul 28, 2026
Q&As: 93 Questions and Answers

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Microsoft DP-750 Exam Syllabus Topics:

Section	Weight	Objectives
Deploy and maintain data pipelines and workloads	30–35%	- Monitor, troubleshoot, and maintain workloads 1. Apply SDLC practices and version control 2. Monitor performance, logs, and execution metrics 3. Troubleshoot failures, repair and restart jobs - 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
Secure and govern Unity Catalog objects	15–20%	- Implement data governance and security 1. Enforce data quality, lineage, and auditing 2. Configure access control: row-level, column-level, attribute-based security 3. Manage catalogs, schemas, tables, views, and volumes - Manage data sharing and permissions 1. Set up external locations and storage credentials 2. Grant and revoke permissions, manage groups and service principals
Set up and configure an Azure Databricks environment	15–20%	- 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. Choose compute types: serverless, job compute, SQL warehouse, classic compute 2. Configure cluster policies, instance pools, and libraries 3. Manage workspace settings, permissions, and networking
Prepare and process data	30–35%	- 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 - 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

Microsoft Implementing Data Engineering Solutions Using Azure Databricks Sample Questions:

1. 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.

2. 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 Lakeflow Connect pipeline and connect it to DB1. Does this meet the goal?

A) Yes
B) No

3. 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.filter(df.order_amount != None)
Does this meet the goal?

A) Yes
B) No

4. You have an Azure Databricks workspace named Workspace! that uses a Git repository. The repository contains a Databricks notebook named Notebook1.
From the main branch, you create a feature branch named Branch! and commit changes to Notebooks Another user commits changes to Notebook1 in main.
When you attempt to merge Branch! into main, the merge fails due to conflicts.
You need to merge Branch! into the main branch. The solution must ensure that Notebook1 includes all the changes from both the branches.
What should you do?

A) From Workspace1, clone the mam branch as a new repository.
B) Apply the changes directly to the main branch.
C) Apply the main branch changes to Branch! and resolve the conflicts.
D) From Workspace1, clone Branch! as a new repository.

5. You have an Azure Databricks workspace that is enabled for Unity Catalog.
You need to ensure that data lineage is captured and can be reviewed for tables accessed by Databricks notebooks and jobs. The solution must minimize administrative effort.
Which compute configuration should you use to capture the data lineage, and what should you use to review the data lineage? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.