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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: Azure Databricks Data Engineer Associate |
| Available Languages: | English |
| Sample Questions: |  |
| 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:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Deploy and maintain data pipelines and workloads | 30-35% | - Manage production workloads
|
| Topic 2: Secure and govern Unity Catalog objects | 15-20% | - Implement governance and security
|
| Topic 3: Set up and configure an Azure Databricks environment | 15-20% | - Create and configure Azure Databricks workspaces
|
| Topic 4: Prepare and process data | 30-35% | - Ingest and transform data
|
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The DP-750 exam is the official assessment for the Microsoft Implementing Data Engineering Solutions Using Azure Databricks certification from Microsoft. Across the wide landscape of IT certifications, this credential verifies that you can apply its published objectives in realistic scenarios — and since most candidates aim to succeed in one focused attempt, the quality of your practice material is the variable that matters most.
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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.
The official outline organizes the DP-750 exam into weighted domains, including:
- Deploy and maintain data pipelines and workloads (30-35%)
- Secure and govern Unity Catalog objects (15-20%)
- Set up and configure an Azure Databricks environment (15-20%)
The Microsoft Implementing Data Engineering Solutions Using Azure Databricks practice questions at ExamTorrent are built around these same objectives — request the free demo by email to see how the content maps onto them.
Microsoft Implementing Data Engineering Solutions Using Azure Databricks Sample Questions:
You have an Azure Databricks workspace that is attached to a Unity Catalog metastore named metastore1.
metastore1 contains:
* A catalog named Sales
* A schema named Customers in the Sales catalog
* A table named Customer_details in the Customers schema
You need to ensure that a user named User1 can update the data in Customer_details. The solution must meet the following requirements:
* Ensure that User1 cannot create new tables.
* Follow the principle of least privilege.
Which permission should you grant to User1 for each object? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Correct Answer:
Explanation:
User1 requires permissions at each level of the Unity Catalog hierarchy. USE CATALOG allows User1 to interact with objects contained in the Sales catalog. USE SCHEMA permits access to objects within the Customers schema. Neither permission independently grants access to the data.
At the table level, MODIFY permits User1 to insert, update, and delete data in Customer_details. It does not grant permission to create new tables. The CREATE TABLE privilege is deliberately not granted on the Customers schema, ensuring that User1 cannot create tables. MANAGE would provide unnecessarily broad administrative capabilities, including the ability to manage permissions, and would violate the principle of least privilege. SELECT provides read access but does not authorize updates. Azure Databricks Therefore, the minimum permissions available in the answer area are USE CATALOG on Sales, USE SCHEMA on Customers, and MODIFY on Customer_details.
You have a Declarative Automation Bundle.
You plan to use the Databricks CLI to deploy the bundle to an environment named dev.
How should you complete the CLI command? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Correct Answer:
Explanation:
The databricks bundle command group is used to validate, deploy, run, and manage Declarative Automation Bundles. The deploy subcommand deploys the bundle's defined resources-such as Lakeflow jobs, pipelines, dashboards, files, and notebooks-to the target Azure Databricks workspace.
The --target dev option selects the target named dev from the targets mapping in the bundle's databricks.yml configuration. Therefore, the complete command is databricks bundle deploy --target dev. The shorter -t dev syntax is also valid, but the answer area provides the full --target form. Microsoft Learn databricks workspace manages workspace objects and is not the command group for deploying bundles. az databricks workspace manages Azure Databricks workspace resources through the Azure CLI. The run dev option would attempt to execute a deployed bundle resource, while sync --target dev synchronizes local files with the workspace but does not deploy the complete bundle configuration.
You have an Azure Databricks workspace that is enabled for Unity Catalog.
You need to profile a table to meet the following requirements:
The count of null values per column must be evaluated repeatedly as new records are added to the table.
Changes in the count of null values must be observable over the progression of the dataset.
Which type of profile should you create?
- A. inference
- B. snapshot
- C. time series
Correct Answer: C 🗳️
Explanation: Only visible for ExamTorrent members. You can sign-up / login (it's free).
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 🗳️
Explanation: Only visible for ExamTorrent members. You can sign-up / login (it's free).
You have an Azure Databricks workspace that is enabled for Unity Catalog and contains two managed Delta tables named sales.schema1.table1 and sales.schema1.table2.
sales.schema1.table1 contains sales data from the current year.
sales.schema1.table2 contains historical data.
You need to load all the rows from sales.schema1.table1 into sales.schema1.table2. The solution must preserve any existing data in sales.schema1.table2 and minimize processing effort.
Which command should you run?
- A. INSERT INTO sales.schema1.table2 SELECT * FROM sales.schema1.table1;
- B. CREATE TABLE sales.schema1.table2 AS SELECT * FROM sales.schema1.table1;
- C. CREATE OR REPLACE TABLE sales.schema1.table2 AS SELECT * FROM sales.schema1.table1;
- D. INSERT OVERWRITE sales.schema1.table2 SELECT * FROM sales.schema1.table1;
Correct Answer: A 🗳️
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