DP-700 Practice Questions and Answers Latest 2026 : Microsoft Fabric Exam Prep

Welcome to Part 3 of our DP-700 practice questions and answers series. This post includes questions 21–30 for the Microsoft DP-700: Implementing Data Engineering Solutions Using Microsoft Fabric exam.

In this set, you will practice key Microsoft Fabric topics such as workspace roles, lakehouse sharing, SQL endpoint access, endorsement badges, domain behavior, deployment pipeline item pairing, workspace folders, Fabric environments, Delta table optimization, Monitoring hub, and Fabric Monitor.

DP-700 Practice Questions and Answers – Part 3

Question 21: Assigning a Workspace Role for Warehouse Table Updates

You have a Fabric workspace named Workspace1 that contains a warehouse named DW1 and a data pipeline named Pipeline1.

You plan to add a user named User3 to Workspace1. You need to ensure that User3 can perform the following actions:

• View all the items in Workspace1.
• Update the tables in DW1.

The solution must follow the principle of least privilege. You already assigned the appropriate object-level permissions to DW1.

Which workspace role should you assign to User3?

1. Admin
2. Member
3. Viewer
4. Contributor

Correct Answer: B. Member

Explanation

The Member role allows User3 to view and interact with items in the workspace. Since the required object-level permissions on DW1 have already been assigned, the workspace role only needs to provide suitable workspace access.

The Viewer role is too limited for this scenario because User3 must update warehouse tables. Admin is too broad and does not follow least privilege. Contributor may allow more item creation and editing than required. Therefore, Member is the best fit in this scenario.

Question 22: Providing SQL-Only Access to a Lakehouse

You have a Fabric capacity that contains a workspace named Workspace1. Workspace1 contains a lakehouse named Lakehouse1, a data pipeline, a notebook, and several Power BI reports.

A user named User1 wants to use SQL to analyze the data in Lakehouse1.

You need to configure access for User1. The solution must meet the following requirements:

• Provide User1 with read access to the table data in Lakehouse1.
• Prevent User1 from using Apache Spark to query the underlying files in Lakehouse1.
• Prevent User1 from accessing other items in Workspace1.

What should you do?

1. Share Lakehouse1 with User1 directly and select Read all SQL endpoint data.
2. Assign User1 the Viewer role for Workspace1. Share Lakehouse1 with User1 and select Read all SQL endpoint data.
3. Share Lakehouse1 with User1 directly and select Build reports on the default semantic model.
4. Assign User1 the Member role for Workspace1. Share Lakehouse1 with User1 and select Read all SQL endpoint data.

Correct Answer: A. Share Lakehouse1 with User1 directly and select Read all SQL endpoint data.

Explanation

The best solution is to share the lakehouse directly with User1 and grant Read all SQL endpoint data. This gives User1 access to query the lakehouse tables through SQL without giving access to the rest of the workspace.

Assigning Viewer or Member at the workspace level would expose additional workspace items, which violates the requirement to prevent access to other items. Granting Spark access would also be too broad because it could allow access to underlying files. Direct lakehouse sharing with SQL endpoint access follows the principle of least privilege.

Question 23: Applying Endorsement Badges in Microsoft Fabric

You are implementing the following data entities in a Fabric environment:

• Entity1: Available in a lakehouse and contains data that will be used as a core organization entity.
• Entity2: Available in a semantic model and contains data that meets organizational standards.
• Entity3: Available in a Power BI report and contains data that is ready for sharing and reuse.
• Entity4: Available in a Power BI dashboard and contains approved data for executive-level decision making.

Your company requires governance processes to be implemented for the data. You need to apply endorsement badges to the entities based on each entity’s use case.

Correct Answer:

• Entity1: Master Data
• Entity2: Certified
• Entity3: Promoted
• Entity4: Cannot be endorsed

Explanation

Master Data is appropriate for Entity1 because it is a core organizational entity stored in a lakehouse. This type of data is central to business operations and can be reused across teams.

Certified is appropriate for Entity2 because the semantic model meets organizational standards. Certification indicates that the data has been validated and approved by the organization.

Promoted is appropriate for Entity3 because the report is ready for sharing and reuse but may not have gone through the full certification process.

Cannot be endorsed applies to Entity4 in this question’s context because endorsement behavior depends on the Fabric item type and governance support for that item.

Question 24: Understanding Domain and Workspace Access

You have three users named User1, User2, and User3. You have Fabric workspaces and a security group named Group1 that contains User1 and User3.

The Fabric admin creates domains. User1 creates a new workspace named Workspace3. You add Group1 to the default domain of Domain1.

For each statement, select whether it is true or false:

• User3 has Viewer role access to Workspace3.
• User3 has Domain Contributor access to Domain1.
• User2 has Contributor role access to Workspace3.

Correct Answer: Yes / Yes / No

Explanation

User3 is a member of Group1. Since Group1 is added to the default domain of Domain1, User3 receives the domain-related access that applies through that group membership.

User3 has Viewer access to Workspace3 and Domain Contributor access to Domain1 according to the scenario. User2 is not included in Group1 and is not granted the same access, so User2 does not have Contributor access to Workspace3.

Question 25: Overwriting Paired Items in a Deployment Pipeline

You have two Fabric workspaces named Workspace1 and Workspace2. You have a Fabric deployment pipeline named deployPipeline1 that deploys items from Workspace1 to Workspace2.

DeployPipeline1 contains all the items in Workspace1. You recently modified the items in Workspace1. Items in Workspace1 that have the same name as items in Workspace2 are currently paired.

You need to ensure that the items in Workspace1 overwrite the corresponding items in Workspace2. The solution must minimize effort.

What should you do?

1. Delete all the items in Workspace2, and then run deployPipeline1.
2. Rename each item in Workspace2 to have the same name as the items in Workspace1.
3. Back up the items in Workspace2, and then run deployPipeline1.
4. Run deployPipeline1 without modifying the items in Workspace2.

Correct Answer: D. Run deployPipeline1 without modifying the items in Workspace2.

Explanation

When items are already paired between deployment pipeline stages, running the deployment updates the paired target items with changes from the source stage.

Because the items in Workspace1 and Workspace2 are already paired, there is no need to rename, delete, or manually recreate items. Running the deployment pipeline is the lowest-effort solution and will overwrite the corresponding paired items in Workspace2.

Question 26: Deploying Workspace Folder Structure

You have a Fabric workspace named Workspace1 that contains a data pipeline named Pipeline1 and a lakehouse named Lakehouse1.

You have a deployment pipeline named deployPipeline1 that deploys Workspace1 to Workspace2.

You restructure Workspace1 by adding a folder named Folder1 and moving Pipeline1 to Folder1. You use deployPipeline1 to deploy Workspace1 to Workspace2.

What occurs to Workspace2?

1. Folder1 is created, Pipeline1 moves to Folder1, and Lakehouse1 is deployed.
2. Only Pipeline1 and Lakehouse1 are deployed.
3. Folder1 is created, and Pipeline1 and Lakehouse1 move to Folder1.
4. Only Folder1 is created and Pipeline1 moves to Folder1.

Correct Answer: A. Folder1 is created, Pipeline1 moves to Folder1, and Lakehouse1 is deployed.

Explanation

Deployment pipelines can preserve workspace organization changes such as folders. Since Pipeline1 was moved into Folder1 in the source workspace, the deployment creates Folder1 in Workspace2 and places Pipeline1 inside it.

Lakehouse1 is also part of the source workspace, so it is included in the deployment process. The correct result is that the folder structure is created, Pipeline1 is placed in Folder1, and Lakehouse1 is deployed.

Question 27: Making Python Libraries Available by Default in New Notebooks

Your company has a team of developers that creates Python libraries of reusable code used to transform data.

You create a Fabric workspace named Workspace1 that will be used to develop ETL solutions by using notebooks.

You need to ensure that the libraries are available by default to new notebooks in Workspace1.

Which three actions should you perform in sequence?

Correct Answer:

1. Create an environment.
2. Install the libraries.
3. Set the default environment.

Explanation

A Fabric environment allows you to manage libraries, dependencies, and runtime configuration for notebooks. To make reusable Python libraries available by default, you first create an environment.

After creating the environment, you install the required libraries into it. Finally, you set that environment as the default environment for the workspace so that new notebooks use it automatically.

This approach avoids manually installing the same libraries in every notebook and supports consistent development across the team.

Question 28: Consolidating Parquet Files in a Delta Table

You have a Fabric workspace that contains a lakehouse and a notebook named Notebook1. Notebook1 reads data into a DataFrame from a table named Table1, applies transformation logic, and writes the data to a new Delta table named Table2 by using a merge operation.

You need to consolidate the underlying Parquet files in Table1.

Which command should you run?

1. VACUUM
2. BROADCAST
3. OPTIMIZE
4. CACHE

Correct Answer: C. OPTIMIZE

Explanation

The OPTIMIZE command is used to compact many small files into fewer larger files in a Delta table. This improves read performance by reducing the number of files that must be scanned during queries.

VACUUM removes old files that are no longer referenced by the Delta transaction log, but it does not compact current files. BROADCAST is a join optimization technique, and CACHE stores data temporarily for faster access. To consolidate Parquet files, OPTIMIZE is the correct command.

Question 29: Identifying the Workspace Where an Item Runs

You have five Fabric workspaces. You are monitoring the execution of items by using Monitoring hub.

You need to identify in which workspace a specific item runs.

Which column should you view in Monitoring hub?

1. Start time
2. Capacity
3. Activity name
4. Submitter
5. Item type
6. Job type
7. Location

Correct Answer: G. Location

Explanation

In Fabric Monitoring hub, the Location column identifies the workspace where an item ran. This is useful when you manage multiple workspaces and need to quickly locate where a job or activity belongs.

Other columns provide useful details such as who submitted the job, when it started, the activity name, or the item type. However, to identify the workspace, you should use the Location column.

Question 30: Identifying the Delta Version Used by a Notebook Execution

You have a Fabric workspace that contains a warehouse named DW1. DW1 is loaded by using a notebook named Notebook1.

You need to identify which version of Delta was used when Notebook1 was executed.

What should you use?

1. Real-Time hub
2. OneLake data hub
3. The Admin monitoring workspace
4. Fabric Monitor
5. The Microsoft Fabric Capacity Metrics app

Correct Answer: D. Fabric Monitor

Explanation

Fabric Monitor provides monitoring and diagnostic details for Fabric workloads, including notebook execution information. It can help you inspect execution metadata and runtime details such as the Delta version used during a notebook run.

Real-Time hub is focused on streaming and event-driven experiences. OneLake data hub is used for discovering and accessing data. The Capacity Metrics app helps monitor capacity usage, but it is not the best option for notebook-level execution details such as Delta version.

Summary of DP-700 Practice Questions Part 3

Frequently Asked Questions

What is the difference between lakehouse SQL endpoint access and Spark access?

SQL endpoint access allows users to query lakehouse tables by using SQL, while Spark access can allow users to interact with underlying files and Spark workloads. SQL endpoint access is often better for least-privilege read-only analytics scenarios.

What does the OPTIMIZE command do in Microsoft Fabric?

The OPTIMIZE command compacts small Parquet files in a Delta table into larger files. This can improve query performance by reducing the number of files scanned during reads.

Why are Fabric environments useful for notebooks?

Fabric environments help manage notebook dependencies, libraries, and runtime settings. Setting a default environment ensures new notebooks use the required libraries automatically.

What does the Location column show in Monitoring hub?

The Location column in Monitoring hub shows the workspace where a specific item or job ran. This is useful when monitoring multiple Fabric workspaces.

What is a paired item in a Fabric deployment pipeline?

A paired item is a source and target item relationship across deployment pipeline stages. When paired items are deployed, the source item updates the matching target item.

Final Thoughts

This third set of DP-700 practice questions covered essential Microsoft Fabric topics such as workspace security, lakehouse sharing, endorsement badges, deployment pipeline behavior, environment configuration, Delta table optimization, and monitoring.

As you continue preparing for DP-700, pay special attention to access control scenarios. Many exam questions test whether you can choose the option that provides enough permissions without exposing unnecessary workspace items, lakehouse files, or sensitive data.