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Microsoft DP-700 Exam Questions
Implementing Data Engineering Solutions Using Microsoft Fabric (Page 3 )

Updated On: 17-May-2026
Page 3 of 16
PDF with 139 Questions

HOTSPOT (Drag and Drop is not supported)
You have a Fabric workspace that contains a warehouse named DW1. DW1 contains the following tables and columns.

You need to create an output that presents the summarized values of all the order quantities by year and product. The results must include a summary of the order quantities at the year level for all the products.
How should you complete the code? To answer, select the appropriate options in the answer area.
Note: Each correct selection is worth one point.
Hot Area:

  1. See Explanation section for answer.

Answer(s): A

Explanation:



Summarize by year and product: The query needs to group data by both year and product name.
Include a summary of order quantities at the year level: This is achieved using grouping mechanisms like ROLLUP or CUBE.
SELECT YEAR(SO.ModifiedDate)
Since we need to summarize the data by year, extracting the year from the ModifiedDate column using YEAR (SO.ModifiedDate) is the correct choice. Other options like CAST or CONVERT do not specifically extract the year.
ROLLUP(YEAR(SO.ModifiedDate), P.Name)
The ROLLUP function creates subtotals for each grouping combination. In this case:
It will group by YEAR(SO.ModifiedDate) and P.Name (product name).
It will also include a summary for all products for each year, which meets the requirement of summarizing order quantities at the year level for all products.



You have a Fabric workspace that contains a lakehouse named Lakehouse1. Data is ingested into Lakehouse1 as one flat table. The table contains the following columns.


You plan to load the data into a dimensional model and implement a star schema. From the original flat table, you create two tables named FactSales and DimProduct. You will track changes in DimProduct.
You need to prepare the data.
Which three columns should you include in the DimProduct table? Each correct answer presents part of the solution.
Note: Each correct selection is worth one point.

  1. Date
  2. ProductName
  3. ProductColor
  4. TransactionID
  5. SalesAmount
  6. ProductID

Answer(s): B,C,F

Explanation:

In a star schema, the DimProduct table serves as a dimension table that contains descriptive attributes about products. It will provide context for the FactSales table, which contains transactional data. The following columns should be included in the DimProduct table:
1. ProductName: The ProductName is an important descriptive attribute of the product, which is needed for analysis and reporting in a dimensional model.
2. ProductColor: ProductColor is another descriptive attribute of the product. In a star schema, it makes sense to include attributes like color in the dimension table to help categorize products in the analysis.
3. ProductID: ProductID is the primary key for the DimProduct table, which will be used to join the FactSales table to the product dimension. It's essential for uniquely identifying each product in the model.



You have a Fabric workspace named Workspace1 that contains a notebook named Notebook1.
In Workspace1, you create a new notebook named Notebook2.
You need to ensure that you can attach Notebook2 to the same Apache Spark session as Notebook1.
What should you do?

  1. Enable high concurrency for notebooks.
  2. Enable dynamic allocation for the Spark pool.
  3. Change the runtime version.
  4. Increase the number of executors.

Answer(s): A

Explanation:

To ensure that Notebook2 can attach to the same Apache Spark session as Notebook1, you need to enable high concurrency for notebooks. High concurrency allows multiple notebooks to share a Spark session, enabling them to run within the same Spark context and thus share resources like cached data, session state, and compute capabilities. This is particularly useful when you need notebooks to run in sequence or together while leveraging shared resources.



You have a Fabric workspace named Workspace1 that contains a lakehouse named Lakehouse1. Lakehouse1 contains the following tables:
Orders
Customer
Employee
The Employee table contains Personally Identifiable Information (PII).
A data engineer is building a workflow that requires writing data to the Customer table, however, the user does NOT have the elevated permissions required to view the contents of the Employee table.
You need to ensure that the data engineer can write data to the Customer table without reading data from the Employee table.
Which three actions should you perform? Each correct answer presents part of the solution.
Note: Each correct selection is worth one point.

  1. Share Lakehouse1 with the data engineer.
  2. Assign the data engineer the Contributor role for Workspace2.
  3. Assign the data engineer the Viewer role for Workspace2.
  4. Assign the data engineer the Contributor role for Workspace1.
  5. Migrate the Employee table from Lakehouse1 to Lakehouse2.
  6. Create a new workspace named Workspace2 that contains a new lakehouse named Lakehouse2.
  7. Assign the data engineer the Viewer role for Workspace1.

Answer(s): D,E,F



You have a Fabric warehouse named DW1. DW1 contains a table that stores sales data and is used by multiple sales representatives.
You plan to implement row-level security (RLS).
You need to ensure that the sales representatives can see only their respective data.
Which warehouse object do you require to implement RLS?

  1. STORED PROCEDURE
  2. CONSTRAINT
  3. SCHEMA
  4. FUNCTION

Answer(s): D

Explanation:

To implement Row-Level Security (RLS) in a Fabric warehouse, you need to use a function that defines the security logic for filtering the rows of data based on the user's identity or role. This function can be used in conjunction with a security policy to control access to specific rows in a table.
In the case of sales representatives, the function would define the filtering criteria (e.g., based on a column such as SalesRepID or SalesRepName), ensuring that each representative can only see their respective data.



HOTSPOT (Drag and Drop is not supported)
You have a Fabric workspace named Workspace1_DEV that contains the following items:
10 reports
Four notebooks
Three lakehouses
Two data pipelines
Two Dataflow Gen1 dataflows
Three Dataflow Gen2 dataflows
Five semantic models that each has a scheduled refresh policy
You create a deployment pipeline named Pipeline1 to move items from Workspace1_DEV to a new workspace named Workspace1_TEST.
You deploy all the items from Workspace1_DEV to Workspace1_TEST.
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
Note: Each correct selection is worth one point.
Hot Area:

  1. See Explanation section for answer.

Answer(s): A

Explanation:



Data from the semantic models will be deployed to the target stage ­ No In a deployment pipeline, data from semantic models (such as cached data) is not typically deployed along with the models themselves.
While the semantic models themselves (including structure and definitions) will be deployed, the actual data (e.g., the results of the model refreshes) will not be automatically transferred. You would need to handle data refreshes separately after the semantic models are deployed.
The Dataflow Gen1 dataflows will be deployed to the target stage ­ Yes Dataflows, including Dataflow Gen1 dataflows, are part of the deployment pipeline and will be deployed to the target stage. These dataflows are part of the solution being deployed, and the pipeline ensures their migration to the target workspace (Workspace1_TEST).
The scheduled refresh policies will be deployed to the target stage ­ No While the scheduled refresh policies for semantic models are part of the configuration, they are not automatically deployed through the pipeline. Deployment pipelines typically move content, such as reports, notebooks, and dataflows, but scheduled refresh settings are not automatically transferred with the deployment. You would need to manually configure the refresh policies in the new workspace (Workspace1_TEST) after the deployment.



You have a Fabric deployment pipeline that uses three workspaces named Dev, Test, and Prod.
You need to deploy an eventhouse as part of the deployment process.
What should you use to add the eventhouse to the deployment process?

  1. GitHub Actions
  2. a deployment pipeline
  3. an Azure DevOps pipeline

Answer(s): C

Explanation:

Correct:
* an Azure DevOps pipeline
Incorrect:
* a deployment pipeline
* an eventstream
* GitHub Actions



You have a Fabric workspace named Workspace1 that contains a warehouse named Warehouse1.
You plan to deploy Warehouse1 to a new workspace named Workspace2.
As part of the deployment process, you need to verify whether Warehouse1 contains invalid references. The solution must minimize development effort.
What should you use?

  1. a database project
  2. a deployment pipeline
  3. a Python script
  4. a T-SQL script

Answer(s): B

Explanation:

A deployment pipeline in Fabric allows you to deploy assets like warehouses, datasets, and reports between different workspaces (such as from Workspace1 to Workspace2). One of the key features of a deployment pipeline is the ability to check for invalid references before deployment. This can help identify issues with assets, such as broken links or dependencies, ensuring the deployment is successful without introducing errors. This is the most efficient way to verify references and manage the deployment with minimal development effort.



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