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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
Passing Score:700
Exam Duration:100 minutes
Exam Format:Case studies, Multiple choice, Scenario-based questions, Multiple select
Available Languages:Chinese (Simplified), Japanese, Portuguese (Brazil), English, French, Korean, German, Spanish
Related Certifications:Microsoft Certified: Azure Developer Associate
Microsoft Certified: Azure Data Engineer Associate
Exam Price:165 USD
Certificate Validity Period:1 year
Real Exam Qty:40–60
Recommended Training:Microsoft Learn Learning Paths
Course DP-750T00-A: Implementing Data Engineering Solutions Using Azure Databricks
Exam Registration:Pearson VUE
Microsoft Learn
Sample Questions: DOWNLOAD DEMO
Exam Way:Online proctored or onsite testing center
Pre Condition:No mandatory prerequisites; recommended experience: data integration, modeling, pipelines, SQL, Python, Azure services, Git
Official Syllabus URL:https://learn.microsoft.com/en-us/credentials/certifications/resources/study-guides/dp-750

Microsoft DP-750 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Prepare and process data30–35%- Ingest and transform data
  • 1. Transform using Spark SQL, PySpark, Scala, and Delta Lake
  • 2. Ingest batch and streaming data from multiple sources
  • 3. Implement schema enforcement, schema drift, and slowly changing dimensions
- Optimize and manage data storage
  • 1. Handle structured, semi-structured, and unstructured data
  • 2. Optimize Delta tables: partitioning, Z-ordering, vacuum, optimize
  • 3. Implement lakehouse architecture and manage table versions
Topic 2: 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. Configure access control: row-level, column-level, attribute-based security
  • 3. Manage catalogs, schemas, tables, views, and volumes
Topic 3: Deploy and maintain data pipelines and workloads30–35%- Build and orchestrate pipelines
  • 1. Implement CI/CD with Git, Databricks Asset Bundles, CLI, and APIs
  • 2. Design and implement Lakeflow Spark Declarative Pipelines
  • 3. Configure Lakeflow Jobs: schedules, triggers, alerts, retries
- Monitor, troubleshoot, and maintain workloads
  • 1. Troubleshoot failures, repair and restart jobs
  • 2. Monitor performance, logs, and execution metrics
  • 3. Apply SDLC practices and version control
Topic 4: Set up and configure an Azure Databricks environment15–20%- 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. Configure cluster policies, instance pools, and libraries
  • 2. Manage workspace settings, permissions, and networking
  • 3. Choose compute types: serverless, job compute, SQL warehouse, classic compute

Microsoft DP-750 Exam Questions Answered

The DP-750 exam leads to the Microsoft Certified: Azure Databricks Data Engineer Associate certification from Microsoft. It is positioned at the Associate level. The exam validates the knowledge and skills expected of candidates preparing for Microsoft Implementing Data Engineering Solutions Using Azure Databricks.

Related certifications include:

  • Microsoft Certified: Azure Data Engineer Associate
  • Microsoft Certified: Azure Developer Associate

The DP-750 exam includes 40–60 questions and allows 100 minutes. Before test day, divide the available time by the number of questions in a timed practice set to establish a steady per-question pace. If a difficult item starts consuming too much time, mark it for review and move forward instead of losing momentum. A full timed practice session can help you decide when to move on, when to return, and how much buffer to reserve for a final check.

The passing score for DP-750 is 700, and the official exam fee is 165 USD. Because a retake requires paying the exam fee again, use timed practice results as a readiness check before scheduling your next attempt. If your performance remains inconsistent, continue reviewing weak domains and repeat a timed session before booking the exam.

No mandatory prerequisites; recommended experience: data integration, modeling, pipelines, SQL, Python, Azure services, Git

Eligibility rules can change, so confirm the current requirements on the official exam page before registering.

You can register through the official channels listed below:

The available exam delivery method is Online proctored or onsite testing center.

The official training options include:

After reviewing the recommended training, reinforce what you learned with Actual4dump's 93 practice questions for DP-750.

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The current DP-750 outline is organized into 4 top-level domains. The first listed areas include:

  • Deploy and maintain data pipelines and workloads (30–35%)
  • Set up and configure an Azure Databricks environment (15–20%)
  • Secure and govern Unity Catalog objects (15–20%)

Review the Exam Topics section above for the complete outline and any nested subtopics.

Microsoft Implementing Data Engineering Solutions Using Azure Databricks Sample Questions:

You have an Azure Databricks workspace that is enabled for Unity Catalog and contains a Delta table named db1.sales_orders.
dbl sales_orders is updated nightly and has change data feed (CDF) enabled.
You need to ingest all the changes from the dbl.sales.ordets table, including inserts, updates, and deletes, into a downstream pipeline.
How should you complete the PsySpark code segment? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Reveal Solution  Discussion  0

Correct Answer:


Explanation:
When Change Data Feed (CDF) is enabled on a Delta table, reading the full change stream - inserts, updates, and deletes - requires this pattern:
spark.readStream.format( ' delta ' ).option( ' readChangeFeed ' , ' true ' ).table( ' db1.sales_orders ' ) The readChangeFeed option switches the reader from the default ' new rows only ' mode to a mode that returns all change events. Each row in the resulting DataFrame includes a _change_type column (insert, update_preimage, update_postimage, delete) so downstream processing can distinguish what happened to each record.
Without readChangeFeed = true, streaming a Delta table only surfaces newly appended rows. Deletes and updates are invisible, making it unsuitable for true CDC pipelines. The stream also supports startingVersion or startingTimestamp options to begin from a specific point in table history rather than the current moment.
Reference: https://learn.microsoft.com/en-us/azure/databricks/delta/delta-change-data-feed

You have an Azure Databricks workspace.
You have an Apache Spark Structured Streaming job named Job! that processes data continuously and fails periodically due to transient errors You need to ensure that Job! meets the following requirements
* Resumes processing from the point that Job1 failed
* Minimizes how long it takes to restart Job!
* Minimizes the costs to restart Job!
What should you do?

  • A. Implement checkpointing.
  • B. Increase the minimum number of nodes in the cluster
  • C. Decrease the retry interval.
  • D. Add an alert and manually restart Job1.
Reveal Solution  Discussion  0

Correct Answer: A  🗳️

Explanation: Only visible for Actual4dump members. You can sign-up / login (it's free).

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.dropna(subset=[ " order_amount " ])
Does this meet the goal?

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

Correct Answer: A  🗳️

Explanation: Only visible for Actual4dump members. You can sign-up / login (it's free).

You have an Azure Databricks workspace that is enabled for Unity Catalog.
You plan to ingest data from CSV files stored in Azure Data Lake Storage Gen2. New rows are appended frequently.
You need to implement a data ingestion solution that meets the following requirements:
* New data must be available in near-real-time (NRT).
* The data must be stored in managed Delta tables.
* The solution must minimize custom code and maintenance effort.
What should you include in the solution?

  • A. scheduled Apache Spark batch jobs
  • B. Auto Loader
  • C. an external table that references the CSV files
  • D. an Azure Data Factory pipeline
Reveal Solution  Discussion  0

Correct Answer: B  🗳️

Explanation: Only visible for Actual4dump members. You can sign-up / login (it's free).

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 Branch! as a new repository.
  • B. Apply the main branch changes to Branch! and resolve the conflicts.
  • C. Apply the changes directly to the main branch.
  • D. From Workspace1, clone the mam branch as a new repository.
Reveal Solution  Discussion  0

Correct Answer: B  🗳️

Explanation: Only visible for Actual4dump members. You can sign-up / login (it's free).

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