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Updated: Oct 18, 2026
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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 |
| Real Exam Qty: | 40–60 |
| Available Languages: | Portuguese (Brazil), Chinese (Simplified), Japanese, German, French, English, Korean, Spanish |
| Exam Format: | Multiple select, Multiple choice, Case studies, Scenario-based questions |
| Related Certifications: | Microsoft Certified: Azure Developer Associate Microsoft Certified: Azure Data Engineer Associate |
| Certificate Validity Period: | 1 year |
| Passing Score: | 700 |
| Exam Price: | 165 USD |
| Exam Duration: | 100 minutes |
| 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: | Microsoft DP-750 Sample Questions |
| 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:
| Section | Weight | Objectives |
|---|---|---|
| Set up and configure an Azure Databricks environment | 15–20% | - Select and configure compute resources
|
| Deploy and maintain data pipelines and workloads | 30–35% | - Monitor, troubleshoot, and maintain workloads
|
| Secure and govern Unity Catalog objects | 15–20% | - Manage data sharing and permissions
|
| Prepare and process data | 30–35% | - Optimize and manage data storage
|
Microsoft Implementing Data Engineering Solutions Using Azure Databricks Sample Questions:
You have an Azure Databricks workspace named Workspace1 that is attached to a Unity Catalog metastore named metastore1 You need to register an Azure Storage account named account1 that has a hierarchical namespace enabled as an external location The external location must use a managed identity to authenticate to account1 and the solution must follow the principle of least privilege.
Which three actions should you perform in sequence ' To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.
Correct Answer:
Explanation:
Registering an ADLS Gen2 account as an external location in Unity Catalog requires three steps in a specific order:
Step 1: Create a Databricks Access Connector. This Azure resource wraps a system-assigned or user-assigned managed identity. It is the credential-less authentication bridge between Databricks and Azure Storage - no SAS tokens or access keys are stored anywhere in the workspace.
Step 2: Create a Storage Credential in Unity Catalog that references the Access Connector. This object tells Unity Catalog ' use this identity when accessing storage. ' The Storage Credential is the reusable authentication object.
Step 3: Create an External Location that maps a Unity Catalog path to the specific ADLS Gen2 container using the Storage Credential. This is the object that grants Databricks users access to files under that path, and it respects the principle of least privilege by scoping access to a specific container.
Reference: https://learn.microsoft.com/en-us/azure/databricks/connect/unity-catalog/storage-credentials
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.filter(df.order_amount != None)
Does this meet the goal?
- A. No
- B. Yes
Correct Answer: A 🗳️
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You need to develop the task logic for a new job in Lakeflow Jobs that processes telemetry data.
Each task must contain only the appropriate logic for its step in the pipeline. The solution must support the planned changes and meet the data ingestion and processing requirements.
What should you do?
- A. Use a single Databricks notebook task that performs ingestion, cleansing, and curation in one script.
- B. Use a single SQL task that performs ingestion, cleansing, and curation by running merge commands.
- C. Create separate tasks for ingestion, cleansing, and curation.
- D. Create three tasks that each contains the identical logic and use task retries.
Correct Answer: C 🗳️
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You have an Azure Databricks workspace that is enabled for Unity Catalog and contains a catalog named CatalogV Catalog1 contains a schema named Schema! and a table named Table1.
You need to ensure that access to the data in Table1 is controlled by using attribute based access control (ABAC).
What should you apply to Table1, and how should you control access for users? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Correct Answer:
Explanation:
Attribute-based access control (ABAC) in Unity Catalog is implemented through row filters. A row filter is a SQL function registered on a table that evaluates the identity or group membership of the querying user and returns only the rows they ' re entitled to see.
The key functions are CURRENT_USER() (returns the logged-in user ' s email) and IS_ACCOUNT_GROUP_MEMBER() (returns true if the user belongs to a specified group). By building filter logic around these, you create access rules that are data-driven - a user in the ' EMEA ' group sees EMEA rows, a user in ' APAC ' sees APAC rows - without maintaining separate table-level grants per data segment.
This is what distinguishes ABAC from role-based access control: decisions are based on the user ' s attributes evaluated at query time, not on static grant lists. The filter is transparent to end users - they query the table normally and only receive rows the policy allows.
Reference: https://learn.microsoft.com/en-us/azure/databricks/data-governance/unity-catalog/row-and-column- filters
You use Databricks Asset Bundles to manage two jobs and an app.
You need to deploy the bundle to development and production environments. The solution must meet the following requirements
* Deploy the app to both environments.
* Deploy only one job to development.
* Minimize administrative effort.
What should you use?
- A. a variables node in a databricks.yml file
- B. a resources node in a databricks.yml file
- C. separate databricks.yml files for each environment
- D. a targets node in a databricks.yml file
Correct Answer: D 🗳️
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