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

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Exam Code: DP-750

Exam Name: Implementing Data Engineering Solutions Using Azure Databricks

Updated: Sep 18, 2026

Q & A: 93 Questions and Answers

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

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Microsoft DP-750 Exam Overview:

Certification Vendor:Microsoft
Exam Name:Implementing Data Engineering Solutions Using Azure Databricks
Exam Number:DP-750
Available Languages:English
Related Certifications:Microsoft Certified: Azure Databricks Data Engineer Associate
Sample Questions:Free Download DP-750 prep4sure exam
Exam Way:Microsoft certification exam delivered through Pearson VUE testing centers and online proctored exam.
Pre Condition:No mandatory prerequisite exam. Candidates should have experience with Azure Databricks, SQL, Python, data engineering concepts, Unity Catalog, Git, Microsoft Entra ID, Azure Data Factory, and Azure Monitor.
Official Syllabus URL:https://learn.microsoft.com/en-us/credentials/certifications/implementing-data-engineering-solutions-using-azure-databricks/

Microsoft DP-750 Exam Syllabus Topics:

SectionWeightObjectives
Deploy and maintain data pipelines and workloads30-35%- Manage production workloads
  • 1. Optimize workload performance and reliability
  • 2. Deploy workloads using Databricks Asset Bundles
  • 3. Maintain production data engineering solutions
  • 4. Implement CI/CD processes
  • 5. Create and manage Lakeflow Jobs
  • 6. Integrate Git-based development workflows
  • 7. Monitor and troubleshoot pipelines
Set up and configure an Azure Databricks environment15-20%- Create and configure Azure Databricks workspaces
  • 1. Configure compute resources and clusters
  • 2. Configure workspace settings
  • 3. Configure networking and connectivity
  • 4. Manage Databricks runtimes
Prepare and process data30-35%- Ingest and transform data
  • 1. Implement streaming data processing
  • 2. Implement data quality controls
  • 3. Model and partition data
  • 4. Implement Delta Lake tables
  • 5. Optimize storage and table performance
  • 6. Transform data using SQL and Python
  • 7. Use Auto Loader and batch ingestion
  • 8. Apply medallion architecture patterns
Secure and govern Unity Catalog objects15-20%- Implement governance and security
  • 1. Implement access control and permissions
  • 2. Implement data-sharing capabilities
  • 3. Manage catalogs, schemas, and tables
  • 4. Configure Unity Catalog
  • 5. Manage data lineage and auditing

DP-750 Exam FAQ: Save Time, Read This

Microsoft Implementing Data Engineering Solutions Using Azure Databricks is an official Microsoft certification exam, listed under the code DP-750. Passing it earns the Microsoft Certified: Fabric Data Engineer Associate certification at the Associate level. It also ties into Microsoft Certified: Azure Databricks Data Engineer Associate. In an era of lifelong learning, this credential is one of the most efficient ways to prove your skills keep pace.

The Microsoft Implementing Data Engineering Solutions Using Azure Databricks syllabus is organized into 4 domains, led by Set up and configure an Azure Databricks environment (15-20%), Secure and govern Unity Catalog objects (15-20%), and Prepare and process data (30-35%). The complete breakdown is above on this page; it is the fastest way to learn where your limited preparation hours belong.

No mandatory prerequisite exam. Candidates should have experience with Azure Databricks, SQL, Python, data engineering concepts, Unity Catalog, Git, Microsoft Entra ID, Azure Data Factory, and Azure Monitor.

Vendor policies are revised periodically, so verify the current requirements before registering via the official exam page.

Yes on both counts. Download the free demo of the Microsoft Implementing Data Engineering Solutions Using Azure Databricks questions first, and browse the comments written by former customers for a second opinion. After purchase, new versions are sent to you as soon as they release, free for 365 days; after expiry, extending the update service costs 50% of the regular price.

Worry-free means a 100% money-back guarantee with stated conditions. Take the Microsoft Implementing Data Engineering Solutions Using Azure Databricks exam within 60 days of purchase; if you fail, you may claim a full refund, provided the exam matches your product. Attempts within 3 days of purchase are ineligible, as are downloaded-but-unused products, free materials, and expired orders; the candidate name must match the payer name. Submit a scanned enrollment slip and the official Score Report PDF within 2 days of the exam, and claims are processed within 7 days. Alternatively, exchange for two other exam products of equal value, free, keeping the update service on your original purchase.

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Microsoft Implementing Data Engineering Solutions Using Azure Databricks Sample Questions:

Question #1

You have an Azure Databricks workspace that is enabled for Unity Catalog and contains a catalog named finance, finance contains two schemas named default and procurement.
You need to create a table named assets in the procurement schema, assets must contain the following columns:
* asset.id
* asset, type
* asset_name
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.

Reveal Solution  Discussion  0

Correct Answer:


Explanation:
The correct SQL statement uses the full three-part namespace finance.procurement.assets with the three specified columns.
In Unity Catalog, every object lives in a three-tier hierarchy: catalog # schema # table. Using the full path finance.procurement.assets guarantees the table lands in the right schema regardless of the session ' s current catalog or schema context. Omitting the catalog or schema name relies on the session default, which may not be finance.procurement - a silent mistake that ' s hard to catch.
The column names asset_id, asset_type, and asset_name must match the spec exactly. Unity Catalog applies access controls, lineage tracking, and tagging at the column level, so the names are meaningful beyond just the schema. Once created, any GRANT statements can target specific columns for fine-grained access control.
Reference: https://learn.microsoft.com/en-us/azure/databricks/sql/language-manual/sql-ref-syntax-ddl-create- table-using

Question #2

You have an Azure Databricks workspace named Workspace1 that contains a takehouse 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 new native catalog in Unity Catalog. Does this meet the goal?

  • A. Yes
  • B. No
Reveal Solution  Discussion  0

Correct Answer: B  🗳️

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Question #3

You have an Azure Databricks workspace that uses Unity Catalog.
You have a Lakeflow Spark Declarative Pipelines (SDP) pipeline that ingests data into a managed Delta table named Table1. Table1 is used for analytics.
New columns are added to the source data, causing pipeline failures during writes to Table1.
You need to prevent the pipeline failures. The solution must ensure that schema changes are detected and handled.
What should you do?

  • A. Enable schema evolution.
  • B. Disable schema enforcement for Table1.
  • C. Use row filters to exclude records that have new columns.
  • D. Create a separate table for each schema version.
Reveal Solution  Discussion  0

Correct Answer: A  🗳️

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Question #4

You have an Azure Databricks workspace that contains a Git folder and uses Azure Repos as the Git provider.
From the main branch, you create a branch named Branch1. You commit changes to Branch1.
You need to incorporate the changes from Branch1 into main The solution must preserve the commit history in the repository. Which command should you run?

  • A. pull
  • B. rebase
  • C. Push
  • D. merge
Reveal Solution  Discussion  0

Correct Answer: D  🗳️

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Question #5

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. a job failure
  • B. task success
  • C. job success
  • D. a task failure
Reveal Solution  Discussion  0

Correct Answer: A  🗳️

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