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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 |
| Related Certifications: | Microsoft Certified: Fabric Data Engineer Associate (DP-700) Microsoft Certified: Azure Data Engineer Associate (DP-203) |
| Passing Score: | 700 |
| Available Languages: | English |
| Exam Format: | Interactive items, Scenario-based questions, Multiple choice, Case studies |
| Exam Duration: | 100 minutes |
| Recommended Training: | DP-750 Training Course (DP-750T00) Microsoft Learn DP-750 Study Guide |
| Exam Registration: | Pearson VUE Exam Scheduling Official Microsoft Certification Page |
| Sample Questions: |  |
| Exam Way: | Online proctored exam via Pearson VUE |
| Pre Condition: | Recommended experience with Azure Databricks, SQL, Python, and basic Azure services (Entra ID, Data Factory, Key Vault, Azure Storage). |
| Official Syllabus URL: | https://learn.microsoft.com/en-us/credentials/certifications/implementing-data-engineering-solutions-using-azure-databricks/ |
Microsoft DP-750 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Prepare and process data | 30-35% | - Data quality and validation
|
| Topic 2: Secure and govern data using Unity Catalog | 15-20% | - Access control and policies
|
| Topic 3: Configure and manage Azure Databricks environments | 15-20% | - Security and authentication setup
|
| Topic 4: Deploy and manage data pipelines and workloads | 30-35% | - Pipeline design and orchestration
|
DP-750 Exam FAQ: Safe, Unlimited, Always Current
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Scoring and fees are published by Microsoft and may change; verify the current figures on the official site.
The DP-750 exam is a Microsoft certification exam validating the Microsoft Implementing Data Engineering Solutions Using Azure Databricks syllabus shown above. It belongs to these credential paths: Microsoft Certified: Azure Data Engineer Associate (DP-203), Microsoft Certified: Fabric Data Engineer Associate (DP-700). Candidates in 2026 start with ExamsTorrent's 93 practice questions, available as a printable PDF, a Windows Desktop Test Engine, and an Online Test Engine — three versions of the same materials.
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The official Microsoft Implementing Data Engineering Solutions Using Azure Databricks outline defines 4 domains. The leading three are Deploy and manage data pipelines and workloads (30-35%), Prepare and process data (30-35%), and Secure and govern data using Unity Catalog (15-20%). The full list is in the topics section above, and ExamsTorrent's 93 practice questions cover each one.
Recommended experience with Azure Databricks, SQL, Python, and basic Azure services (Entra ID, Data Factory, Key Vault, Azure Storage). Since requirements change over time, confirm the latest on the official Microsoft exam page before scheduling.
The official Microsoft page publishes the current question count and duration; review it before your test date.
Microsoft recommends these training resources for the Microsoft Implementing Data Engineering Solutions Using Azure Databricks exam:
Follow the training with steady self-testing — the 93 practice questions from ExamsTorrent help you apply theory and fill any gaps the training leaves.
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Microsoft Implementing Data Engineering Solutions Using Azure Databricks Sample Questions:
You have an Apache Spark DataFrame named salesDF that contains the following columns:
* Product
* Region
* Sales
* Date
You need to create a pivot table that shows the total sales by product for each region.
How should you complete the PySpark code segment? 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.
Correct Answer:
Explanation:
* groupBy
* pivot
The groupBy( " Region " ) operation establishes one aggregation group for each geographical region in the DataFrame. The subsequent pivot( " Product " ) operation converts every distinct Product value into a separate output column. Finally, agg(F.sum( " Sales " )) calculates the total sales for every product within each region. The resulting DataFrame contains one row per region and one column for each product, with the corresponding aggregated sales value. cogroup combines grouped datasets and is not used for creating a conventional pivot table. coalesce controls the number of DataFrame partitions or replaces null values, depending on its context. The sum function is already provided inside the aggregation expression and therefore is not required in either blank.
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.
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 are creating a Lakeflow Spark Declarative Pipelines (SDP) pipeline that scales automatically. You need to configure compute for the pipeline. The solution must minimize operational costs and effort. What should you use?
- A. a single-node, all-purpose cluster
- B. the existing SQL warehouse
- C. an all-purpose cluster that uses autoscaling
- D. a job cluster that uses autoscaling
Correct Answer: D 🗳️
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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. Push
- B. rebase
- C. pull
- D. merge
Correct Answer: D 🗳️
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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. Enable change data feed (CDF) for the target table.
- B. Configure a checkpoint location for the streaming query.
- C. Increase the trigger interval of the streaming query.
- D. Configure a watermark for the streaming query.
Correct Answer: B 🗳️
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