Free DP-600 Exam Practice Questions

Question 1

You have a Fabric tenant that contains a lakehouse named Lakehouse’. Lakehouse1 contains a table named Tablet.
You are creating a new data pipeline.
You plan to copy external data to Table’. The schema of the external data changes regularly.
You need the copy operation to meet the following requirements:
Replace Table1 with the schema of the external data.
Replace all the data in Table1 with the rows in the external data.
You add a Copy data activity to the pipeline.
What should you do for the Copy data activity?

Correct Answer : C. From the Destination tab, set Table action to Overwrite.

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Question 2

HOTSPOT -
You have a Fabric tenant that contains a lakehouse named Lakehouse1. Lakehouse1 contains a table named Nyctaxi_raw. Nyctaxi_row contains the following table:

You create a Fabric notebook and attach it to Lakehouse1.
You need to use PySpark code to transform the data. The solution must meet the following requirements:
Add a column named pickupDate that will contain only the date portion of pickupDateTime.
Filter the DataFrame to include only rows where fareAmount is a positive number that is less than 100.
How should you complete the code? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Correct Answer : D. df.withColumnsRenamed, .cast('date'), .filter("fareAmount > 0 AND farAmount < 100")

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Question 3

You have a Fabric workspace that contains a DirectQuery semantic model. The model queries a data source that has 500 million rows.
You have a Microsoft Power Bi report named Report1 that uses the model. Report1 contains visuals on multiple pages.
You need to reduce the query execution time for the visuals on all the pages.
What are two features that you can use? Each correct answer presents a complete solution,
NOTE: Each correct answer is worth one point.

Correct Answer : B. OneLake integration, C. automatic aggregation

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Question 4

Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution.
After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen.
You have a Fabric tenant that contains a new semantic model in OneLake.
You use a Fabric notebook to read the data into a Spark DataFrame.
You need to evaluate the data to calculate the min, max, mean, and standard deviation values for all the string and numeric columns.
Solution: You use the following PySpark expression:
df.explain()
Does this meet the goal?

Correct Answer : B. No

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Question 5

Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution.
After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen.
You have a Fabric tenant that contains a lakehouse named Lakehouse1. Lakehouse1 contains a Delta table named Customer.
When you query Customer, you discover that the query is slow to execute. You suspect that maintenance was NOT performed on the table.
You need to identify whether maintenance tasks were performed on Customer.
Solution: You run the following Spark SQL statement:

REFRESH TABLE customer -
Does this meet the goal?

Correct Answer : A. No

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Question 6

You have a Fabric tenant that contains a lakehouse named Lakehouse1. Lakehouse1 contains a subfolder named Subfolder1 that contains CSV files.
You need to convert the CSV files into the delta format that has V-Order optimization enabled.
What should you do from Lakehouse explorer?

Correct Answer : B. Use the Load to Tables feature.

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Question 7

HOTSPOT -
You have a Fabric workspace named Workspace1 and an Azure Data Lake Storage Gen2 account named storage1. Workspace1 contains a lakehouse named Lakehouse1.
You need to create a shortcut to storage1 in Lakehouse1.
Which connection and endpoint should you specify? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Correct Answer : D. abfss, dfs

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Question 8

HOTSPOT -

Case study -
This is a case study. Case studies are not timed separately. You can use as much exam time as you would like to complete each case. However, there may be additional case studies and sections on this exam. You must manage your time to ensure that you are able to complete all questions included on this exam in the time provided.
To answer the questions included in a case study, you will need to reference information that is provided in the case study. Case studies might contain exhibits and other resources that provide more information about the scenario that is described in the case study. Each question is independent of the other questions in this case study.
At the end of this case study, a review screen will appear. This screen allows you to review your answers and to make changes before you move to the next section of the exam. After you begin a new section, you cannot return to this section.

To start the case study -
To display the first question in this case study, click the Next button. Use the buttons in the left pane to explore the content of the case study before you answer the questions. Clicking these buttons displays information such as business requirements, existing environment, and problem statements. If the case study has an All Information tab, note that the information displayed is identical to the information displayed on the subsequent tabs. When you are ready to answer a question, click the Question button to return to the question.

Overview -
Contoso, Ltd. is a US-based health supplements company. Contoso has two divisions named Sales and Research. The Sales division contains two departments named Online Sales and Retail Sales. The Research division assigns internally developed product lines to individual teams of researchers and analysts.

Existing Environment -

Identity Environment -
Contoso has a Microsoft Entra tenant named contoso.com. The tenant contains two groups named ResearchReviewersGroup1 and ResearchReviewersGroup2.

Data Environment -
Contoso has the following data environment:
The Sales division uses a Microsoft Power BI Premium capacity.
The semantic model of the Online Sales department includes a fact table named Orders that uses Import made. In the system of origin, the OrderID value represents the sequence in which orders are created.
The Research department uses an on-premises, third-party data warehousing product.
Fabric is enabled for contoso.com.
An Azure Data Lake Storage Gen2 storage account named storage1 contains Research division data for a product line named Productline1. The data is in the delta format.
A Data Lake Storage Gen2 storage account named storage2 contains Research division data for a product line named Productline2. The data is in the CSV format.

Requirements -

Planned Changes -
Contoso plans to make the following changes:
Enable support for Fabric in the Power BI Premium capacity used by the Sales division.
Make all the data for the Sales division and the Research division available in Fabric.
For the Research division, create two Fabric workspaces named Productline1ws and Productine2ws.
In Productline1ws, create a lakehouse named Lakehouse1.
In Lakehouse1, create a shortcut to storage1 named ResearchProduct.

Data Analytics Requirements -
Contoso identifies the following data analytics requirements:
All the workspaces for the Sales division and the Research division must support all Fabric experiences.
The Research division workspaces must use a dedicated, on-demand capacity that has per-minute billing.
The Research division workspaces must be grouped together logically to support OneLake data hub filtering based on the department name.
For the Research division workspaces, the members of ResearchReviewersGroup1 must be able to read lakehouse and warehouse data and shortcuts by using SQL endpoints.
For the Research division workspaces, the members of ResearchReviewersGroup2 must be able to read lakehouse data by using Lakehouse explorer.
All the semantic models and reports for the Research division must use version control that supports branching.

Data Preparation Requirements -
Contoso identifies the following data preparation requirements:
The Research division data for Productline1 must be retrieved from Lakehouse1 by using Fabric notebooks.
All the Research division data in the lakehouses must be presented as managed tables in Lakehouse explorer.

Semantic Model Requirements -
Contoso identifies the following requirements for implementing and managing semantic models:
The number of rows added to the Orders table during refreshes must be minimized.
The semantic models in the Research division workspaces must use Direct Lake mode.

General Requirements -
Contoso identifies the following high-level requirements that must be considered for all solutions:
Follow the principle of least privilege when applicable.
Minimize implementation and maintenance effort when possible.
You need to recommend a solution to group the Research division workspaces.
What should you include in the recommendation? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Correct Answer : A. Domain, OneLake data hub

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Question 9

You have a Fabric tenant that uses a Microsoft Power BI Premium capacity.
You need to enable scale-out for a semantic model.
What should you do first?

Correct Answer : A. At the semantic model level, set Large dataset storage format to On.

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Question 10

Case study -
This is a case study. Case studies are not timed separately. You can use as much exam time as you would like to complete each case. However, there may be additional case studies and sections on this exam. You must manage your time to ensure that you are able to complete all questions included on this exam in the time provided.
To answer the questions included in a case study, you will need to reference information that is provided in the case study. Case studies might contain exhibits and other resources that provide more information about the scenario that is described in the case study. Each question is independent of the other questions in this case study.
At the end of this case study, a review screen will appear. This screen allows you to review your answers and to make changes before you move to the next section of the exam. After you begin a new section, you cannot return to this section.

To start the case study -
To display the first question in this case study, click the Next button. Use the buttons in the left pane to explore the content of the case study before you answer the questions. Clicking these buttons displays information such as business requirements, existing environment, and problem statements. If the case study has an All Information tab, note that the information displayed is identical to the information displayed on the subsequent tabs. When you are ready to answer a question, click the Question button to return to the question.

Overview -
Contoso, Ltd. is a US-based health supplements company. Contoso has two divisions named Sales and Research. The Sales division contains two departments named Online Sales and Retail Sales. The Research division assigns internally developed product lines to individual teams of researchers and analysts.

Existing Environment -

Identity Environment -
Contoso has a Microsoft Entra tenant named contoso.com. The tenant contains two groups named ResearchReviewersGroup1 and ResearchReviewersGroup2.

Data Environment -
Contoso has the following data environment:
The Sales division uses a Microsoft Power BI Premium capacity.
The semantic model of the Online Sales department includes a fact table named Orders that uses Import made. In the system of origin, the OrderID value represents the sequence in which orders are created.
The Research department uses an on-premises, third-party data warehousing product.
Fabric is enabled for contoso.com.
An Azure Data Lake Storage Gen2 storage account named storage1 contains Research division data for a product line named Productline1. The data is in the delta format.
A Data Lake Storage Gen2 storage account named storage2 contains Research division data for a product line named Productline2. The data is in the CSV format.

Requirements -

Planned Changes -
Contoso plans to make the following changes:
Enable support for Fabric in the Power BI Premium capacity used by the Sales division.
Make all the data for the Sales division and the Research division available in Fabric.
For the Research division, create two Fabric workspaces named Productline1ws and Productine2ws.
In Productline1ws, create a lakehouse named Lakehouse1.
In Lakehouse1, create a shortcut to storage1 named ResearchProduct.

Data Analytics Requirements -
Contoso identifies the following data analytics requirements:
All the workspaces for the Sales division and the Research division must support all Fabric experiences.
The Research division workspaces must use a dedicated, on-demand capacity that has per-minute billing.
The Research division workspaces must be grouped together logically to support OneLake data hub filtering based on the department name.
For the Research division workspaces, the members of ResearchReviewersGroup1 must be able to read lakehouse and warehouse data and shortcuts by using SQL endpoints.
For the Research division workspaces, the members of ResearchReviewersGroup2 must be able to read lakehouse data by using Lakehouse explorer.
All the semantic models and reports for the Research division must use version control that supports branching.

Data Preparation Requirements -
Contoso identifies the following data preparation requirements:
The Research division data for Productline1 must be retrieved from Lakehouse1 by using Fabric notebooks.
All the Research division data in the lakehouses must be presented as managed tables in Lakehouse explorer.

Semantic Model Requirements -
Contoso identifies the following requirements for implementing and managing semantic models:
The number of rows added to the Orders table during refreshes must be minimized.
The semantic models in the Research division workspaces must use Direct Lake mode.

General Requirements -
Contoso identifies the following high-level requirements that must be considered for all solutions:
Follow the principle of least privilege when applicable.
Minimize implementation and maintenance effort when possible.
You need to ensure that Contoso can use version control to meet the data analytics requirements and the general requirements.
What should you do?

Correct Answer : B. Modify the settings of the Research workspaces to use a GitHub repository.

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