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

DP-750 real exams

Exam Code: DP-750

Exam Name: Implementing Data Engineering Solutions Using Azure Databricks

Updated: Jul 30, 2026

Q & A: 93 Questions and Answers

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DP-750 exam dumps

Microsoft DP-750 Exam Syllabus Topics:

SectionWeightObjectives
Prepare and process data30–35%- Optimize and manage data storage
  • 1. Handle structured, semi-structured, and unstructured data
  • 2. Implement lakehouse architecture and manage table versions
  • 3. Optimize Delta tables: partitioning, Z-ordering, vacuum, optimize
- Ingest and transform data
  • 1. Ingest batch and streaming data from multiple sources
  • 2. Implement schema enforcement, schema drift, and slowly changing dimensions
  • 3. Transform using Spark SQL, PySpark, Scala, and Delta Lake
Set up and configure an Azure Databricks environment15–20%- 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
- Select and configure compute resources
  • 1. Manage workspace settings, permissions, and networking
  • 2. Choose compute types: serverless, job compute, SQL warehouse, classic compute
  • 3. Configure cluster policies, instance pools, and libraries
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
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. Enforce data quality, lineage, and auditing
  • 2. Manage catalogs, schemas, tables, views, and volumes
  • 3. Configure access control: row-level, column-level, attribute-based security

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:
* A catalog named Corpdb
* A schema named Finance in the Corpdb catalog
* A table named Sales in the Finance schema
You have a group named Analysts.
You assign the following permissions to Analysts:
* USE CATALOG on the Corpdb catalog
* USE SCHEMA on the Finance schema
* SELECT on the Sales table
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.


2. You have an Azure Databricks workspace that is enabled for Unity Catalog You have an Apache Spark Structured Streaming job that writes data to a Delta table.
After the cluster restarts, the streaming job reprocesses previously ingested data You need to prevent the streaming job from reprocessing the data after the cluster restarts.
What should you do?

A) Configure a watermark for the streaming query.
B) Increase the trigger interval of the streaming query.
C) Enable change data feed (CDF) for the target table.
D) Configure a checkpoint location for the streaming query.


3. You have an Azure Databricks workspace that is enabled for Unity Catalog and contains two Delta tables named Table1 and Table2 of the same data type.
Table1 contains a column named Columnl. Table2 contains a column named Column2. You run the following query.
SELECT Column1
FROM Table1
GROUP BY Column1
HAVING COUNT( " ) > 1
INTERSECT
SELECT C0lumn2
FROM Table2
GROUP BY Column2
HAVING COUNT( ' ) > 1;
What occurs when you run the query?

A) Values appear in both tables more than once.
B) Values appear in either table more than once.
C) Values appear in Table2 but NOT Table1.
D) Values appear in Table more than once.


4. You have an Azure Databricks account that contains a single workspace named Workspace1. Workspace1 is enabled for Unity Catalog.
You discover that data access events for Unity Catalog tables fail to appear in the logs.
You need to ensure that all the data access events are captured centrally for auditing purposes. The log data must be available for analysis as quickly as possible.
What should you do? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.


5. 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) Create a custom dependency management script and run the script from a Databricks notebook.
D) Install the libraries by using pip3.


Solutions:

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

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