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DP-600 Practice Questions

Fabric Analytics Engineer Associate

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Question 1

Overview

Litware, Inc. is a manufacturing company that has offices throughout North America. The analytics team at Litware contains data engineers, analytics engineers, data analysts, and data scientists.

Existing Environment

Fabric Environment

Litware has been using a Microsoft Power BI tenant for three years. Litware has NOT enabled any Fabric capacities and features.

Available Data

Description

Original source

Total size

Customer data

Customer relationship management (CRM) system

50 MB

Product data

Customer relationship management (CRM) system

200 MB

Customer satisfaction surveys

SurveyMonkey

500 GB

Name

Data type

ProductID

Integer

ProductName

String

ProductCategory

String

ListPrice

Decimal
  • Survey
  • Question
  • Response
  • One row is added to the Survey table.
  • One row is added to the Response table for each question in the survey.

User Problems

The analytics team has large volumes of data, some of which is semi-structured. The team wants to use Fabric to create a new data store.

 

Product data is often classified into three pricing groups: high, medium, and low. This logic is implemented in several databases and semantic models, but the logic does NOT always match across implementations.

Requirements

Planned Changes

  • AnalyticsPOC: Will contain the data store, semantic models, reports, pipelines, dataflows, and notebooks used to populate the data store
  • DataEngPOC: Will contain all the pipelines, dataflows, and notebooks used to populate OneLake
  • DataSciPOC: Will contain all the notebooks and reports created by the data scientists
  • A data store (type to be decided)
  • A custom semantic model
  • Interactive reports

Technical Requirements

  • Read access by using T-SQL or Python
  • Semi-structured and unstructured data
  • Row-level security (RLS) for users executing T-SQL queries
  • List prices that are less than or equal to 50 are in the low pricing group.
  • List prices that are greater than 50 and less than or equal to 1,000 are in the medium pricing group.
  • List prices that are greater than 1,000 are in the high pricing group.

Security Requirements

  • Fabric administrators will be the workspace administrators.
  • The data engineers must be able to read from the data store. No access must be granted to datasets or reports.
  • The analytics engineers must be able to read from, write to, and create schemas in the data store. They also must be able to create and share semantic models with the data analysts and view and modify all reports in the workspace.
  • The data scientists must be able to read from the data store, but not write to it. They will access the data by using a Spark notebook.
  • The data analysts must have read access to only the dimensional model objects in the data store. They also must have access to create Power BI reports by using the semantic models created by the analytics engineers.
  • The date dimension must be available to all users of the data store.
  • The principle of least privilege must be followed.
  • FabricAdmins: Fabric administrators
  • AnalyticsTeam: All the members of the analytics team
  • DataAnalysts: The data analysts on the analytics team
  • DataScientists: The data scientists on the analytics team
  • DataEngineers: The data engineers on the analytics team
  • AnalyticsEngineers: The analytics engineers on the analytics team

Report Requirements

  • Enables a user to select a product to filter customer survey responses to only those who have purchased that product
  • Displays the average overall satisfaction score of all the surveys submitted during the last 12 months up to a selected date
  • Shows data as soon as the data is updated in the data store
  • Ensures that the report and the semantic model only contain data from the current and previous year
  • Ensures that the report respects any table-level security specified in the source data store
  • Minimizes the execution time of report queries

You need to implement the date dimension in the data store. The solution must meet the technical requirements.

 

What are two ways to achieve the goal? Each correct answer presents a complete solution.

 

NOTE: Each correct selection is worth one point.

Select an answer above, then submit your vote. Reading or practicing does not cast a vote.

Question 2

Overview

Litware, Inc. is a manufacturing company that has offices throughout North America. The analytics team at Litware contains data engineers, analytics engineers, data analysts, and data scientists.

Existing Environment

Fabric Environment

Litware has been using a Microsoft Power BI tenant for three years. Litware has NOT enabled any Fabric capacities and features.

Available Data

Description

Original source

Total size

Customer data

Customer relationship management (CRM) system

50 MB

Product data

Customer relationship management (CRM) system

200 MB

Customer satisfaction surveys

SurveyMonkey

500 GB

Name

Data type

ProductID

Integer

ProductName

String

ProductCategory

String

ListPrice

Decimal
  • Survey
  • Question
  • Response
  • One row is added to the Survey table.
  • One row is added to the Response table for each question in the survey.

User Problems

The analytics team has large volumes of data, some of which is semi-structured. The team wants to use Fabric to create a new data store.

 

Product data is often classified into three pricing groups: high, medium, and low. This logic is implemented in several databases and semantic models, but the logic does NOT always match across implementations.

Requirements

Planned Changes

  • AnalyticsPOC: Will contain the data store, semantic models, reports, pipelines, dataflows, and notebooks used to populate the data store
  • DataEngPOC: Will contain all the pipelines, dataflows, and notebooks used to populate OneLake
  • DataSciPOC: Will contain all the notebooks and reports created by the data scientists
  • A data store (type to be decided)
  • A custom semantic model
  • Interactive reports

Technical Requirements

  • Read access by using T-SQL or Python
  • Semi-structured and unstructured data
  • Row-level security (RLS) for users executing T-SQL queries
  • List prices that are less than or equal to 50 are in the low pricing group.
  • List prices that are greater than 50 and less than or equal to 1,000 are in the medium pricing group.
  • List prices that are greater than 1,000 are in the high pricing group.

Security Requirements

  • Fabric administrators will be the workspace administrators.
  • The data engineers must be able to read from the data store. No access must be granted to datasets or reports.
  • The analytics engineers must be able to read from, write to, and create schemas in the data store. They also must be able to create and share semantic models with the data analysts and view and modify all reports in the workspace.
  • The data scientists must be able to read from the data store, but not write to it. They will access the data by using a Spark notebook.
  • The data analysts must have read access to only the dimensional model objects in the data store. They also must have access to create Power BI reports by using the semantic models created by the analytics engineers.
  • The date dimension must be available to all users of the data store.
  • The principle of least privilege must be followed.
  • FabricAdmins: Fabric administrators
  • AnalyticsTeam: All the members of the analytics team
  • DataAnalysts: The data analysts on the analytics team
  • DataScientists: The data scientists on the analytics team
  • DataEngineers: The data engineers on the analytics team
  • AnalyticsEngineers: The analytics engineers on the analytics team

Report Requirements

  • Enables a user to select a product to filter customer survey responses to only those who have purchased that product
  • Displays the average overall satisfaction score of all the surveys submitted during the last 12 months up to a selected date
  • Shows data as soon as the data is updated in the data store
  • Ensures that the report and the semantic model only contain data from the current and previous year
  • Ensures that the report respects any table-level security specified in the source data store
  • Minimizes the execution time of report queries

You need to create a DAX measure to calculate the average overall satisfaction score.

 

How should you complete the DAX code? To answer, select the appropriate options in the answer area.

 

NOTE: Each correct selection is worth one point.
Rolling 12 Overall Satisfaction =
VAR NumberOfMonths = 12
VAR LastCurrentDate = MAX ( 'Date'[Date] )
VAR Period = DATESINPERIOD ( 'Date'[Date], LastCurrentDate, - NumberOfMonths, MONTH )
VAR Result =
CALCULATE (
'Survey Question'[Question Title] = "Overall Satisfaction"
)
RETURN
Result

Question 3

Overview

Litware, Inc. is a manufacturing company that has offices throughout North America. The analytics team at Litware contains data engineers, analytics engineers, data analysts, and data scientists.

Existing Environment

Fabric Environment

Litware has been using a Microsoft Power BI tenant for three years. Litware has NOT enabled any Fabric capacities and features.

Available Data

Description

Original source

Total size

Customer data

Customer relationship management (CRM) system

50 MB

Product data

Customer relationship management (CRM) system

200 MB

Customer satisfaction surveys

SurveyMonkey

500 GB

Name

Data type

ProductID

Integer

ProductName

String

ProductCategory

String

ListPrice

Decimal
  • Survey
  • Question
  • Response
  • One row is added to the Survey table.
  • One row is added to the Response table for each question in the survey.

User Problems

The analytics team has large volumes of data, some of which is semi-structured. The team wants to use Fabric to create a new data store.

 

Product data is often classified into three pricing groups: high, medium, and low. This logic is implemented in several databases and semantic models, but the logic does NOT always match across implementations.

Requirements

Planned Changes

  • AnalyticsPOC: Will contain the data store, semantic models, reports, pipelines, dataflows, and notebooks used to populate the data store
  • DataEngPOC: Will contain all the pipelines, dataflows, and notebooks used to populate OneLake
  • DataSciPOC: Will contain all the notebooks and reports created by the data scientists
  • A data store (type to be decided)
  • A custom semantic model
  • Interactive reports

Technical Requirements

  • Read access by using T-SQL or Python
  • Semi-structured and unstructured data
  • Row-level security (RLS) for users executing T-SQL queries
  • List prices that are less than or equal to 50 are in the low pricing group.
  • List prices that are greater than 50 and less than or equal to 1,000 are in the medium pricing group.
  • List prices that are greater than 1,000 are in the high pricing group.

Security Requirements

  • Fabric administrators will be the workspace administrators.
  • The data engineers must be able to read from the data store. No access must be granted to datasets or reports.
  • The analytics engineers must be able to read from, write to, and create schemas in the data store. They also must be able to create and share semantic models with the data analysts and view and modify all reports in the workspace.
  • The data scientists must be able to read from the data store, but not write to it. They will access the data by using a Spark notebook.
  • The data analysts must have read access to only the dimensional model objects in the data store. They also must have access to create Power BI reports by using the semantic models created by the analytics engineers.
  • The date dimension must be available to all users of the data store.
  • The principle of least privilege must be followed.
  • FabricAdmins: Fabric administrators
  • AnalyticsTeam: All the members of the analytics team
  • DataAnalysts: The data analysts on the analytics team
  • DataScientists: The data scientists on the analytics team
  • DataEngineers: The data engineers on the analytics team
  • AnalyticsEngineers: The analytics engineers on the analytics team

Report Requirements

  • Enables a user to select a product to filter customer survey responses to only those who have purchased that product
  • Displays the average overall satisfaction score of all the surveys submitted during the last 12 months up to a selected date
  • Shows data as soon as the data is updated in the data store
  • Ensures that the report and the semantic model only contain data from the current and previous year
  • Ensures that the report respects any table-level security specified in the source data store
  • Minimizes the execution time of report queries
Which type of data store should you recommend in the AnalyticsPOC workspace?

Select an answer above, then submit your vote. Reading or practicing does not cast a vote.

Question 4

Overview

Litware, Inc. is a manufacturing company that has offices throughout North America. The analytics team at Litware contains data engineers, analytics engineers, data analysts, and data scientists.

Existing Environment

Fabric Environment

Litware has been using a Microsoft Power BI tenant for three years. Litware has NOT enabled any Fabric capacities and features.

Available Data

Description

Original source

Total size

Customer data

Customer relationship management (CRM) system

50 MB

Product data

Customer relationship management (CRM) system

200 MB

Customer satisfaction surveys

SurveyMonkey

500 GB

Name

Data type

ProductID

Integer

ProductName

String

ProductCategory

String

ListPrice

Decimal
  • Survey
  • Question
  • Response
  • One row is added to the Survey table.
  • One row is added to the Response table for each question in the survey.

User Problems

The analytics team has large volumes of data, some of which is semi-structured. The team wants to use Fabric to create a new data store.

 

Product data is often classified into three pricing groups: high, medium, and low. This logic is implemented in several databases and semantic models, but the logic does NOT always match across implementations.

Requirements

Planned Changes

  • AnalyticsPOC: Will contain the data store, semantic models, reports, pipelines, dataflows, and notebooks used to populate the data store
  • DataEngPOC: Will contain all the pipelines, dataflows, and notebooks used to populate OneLake
  • DataSciPOC: Will contain all the notebooks and reports created by the data scientists
  • A data store (type to be decided)
  • A custom semantic model
  • Interactive reports

Technical Requirements

  • Read access by using T-SQL or Python
  • Semi-structured and unstructured data
  • Row-level security (RLS) for users executing T-SQL queries
  • List prices that are less than or equal to 50 are in the low pricing group.
  • List prices that are greater than 50 and less than or equal to 1,000 are in the medium pricing group.
  • List prices that are greater than 1,000 are in the high pricing group.

Security Requirements

  • Fabric administrators will be the workspace administrators.
  • The data engineers must be able to read from the data store. No access must be granted to datasets or reports.
  • The analytics engineers must be able to read from, write to, and create schemas in the data store. They also must be able to create and share semantic models with the data analysts and view and modify all reports in the workspace.
  • The data scientists must be able to read from the data store, but not write to it. They will access the data by using a Spark notebook.
  • The data analysts must have read access to only the dimensional model objects in the data store. They also must have access to create Power BI reports by using the semantic models created by the analytics engineers.
  • The date dimension must be available to all users of the data store.
  • The principle of least privilege must be followed.
  • FabricAdmins: Fabric administrators
  • AnalyticsTeam: All the members of the analytics team
  • DataAnalysts: The data analysts on the analytics team
  • DataScientists: The data scientists on the analytics team
  • DataEngineers: The data engineers on the analytics team
  • AnalyticsEngineers: The analytics engineers on the analytics team

Report Requirements

  • Enables a user to select a product to filter customer survey responses to only those who have purchased that product
  • Displays the average overall satisfaction score of all the surveys submitted during the last 12 months up to a selected date
  • Shows data as soon as the data is updated in the data store
  • Ensures that the report and the semantic model only contain data from the current and previous year
  • Ensures that the report respects any table-level security specified in the source data store
  • Minimizes the execution time of report queries
What should you recommend using to ingest the customer data into the data store in the AnalyticsPOC workspace?

Select an answer above, then submit your vote. Reading or practicing does not cast a vote.

Question 5

Overview

Litware, Inc. is a manufacturing company that has offices throughout North America. The analytics team at Litware contains data engineers, analytics engineers, data analysts, and data scientists.

Existing Environment

Fabric Environment

Litware has been using a Microsoft Power BI tenant for three years. Litware has NOT enabled any Fabric capacities and features.

Available Data

Description

Original source

Total size

Customer data

Customer relationship management (CRM) system

50 MB

Product data

Customer relationship management (CRM) system

200 MB

Customer satisfaction surveys

SurveyMonkey

500 GB

Name

Data type

ProductID

Integer

ProductName

String

ProductCategory

String

ListPrice

Decimal
  • Survey
  • Question
  • Response
  • One row is added to the Survey table.
  • One row is added to the Response table for each question in the survey.

User Problems

The analytics team has large volumes of data, some of which is semi-structured. The team wants to use Fabric to create a new data store.

 

Product data is often classified into three pricing groups: high, medium, and low. This logic is implemented in several databases and semantic models, but the logic does NOT always match across implementations.

Requirements

Planned Changes

  • AnalyticsPOC: Will contain the data store, semantic models, reports, pipelines, dataflows, and notebooks used to populate the data store
  • DataEngPOC: Will contain all the pipelines, dataflows, and notebooks used to populate OneLake
  • DataSciPOC: Will contain all the notebooks and reports created by the data scientists
  • A data store (type to be decided)
  • A custom semantic model
  • Interactive reports

Technical Requirements

  • Read access by using T-SQL or Python
  • Semi-structured and unstructured data
  • Row-level security (RLS) for users executing T-SQL queries
  • List prices that are less than or equal to 50 are in the low pricing group.
  • List prices that are greater than 50 and less than or equal to 1,000 are in the medium pricing group.
  • List prices that are greater than 1,000 are in the high pricing group.

Security Requirements

  • Fabric administrators will be the workspace administrators.
  • The data engineers must be able to read from the data store. No access must be granted to datasets or reports.
  • The analytics engineers must be able to read from, write to, and create schemas in the data store. They also must be able to create and share semantic models with the data analysts and view and modify all reports in the workspace.
  • The data scientists must be able to read from the data store, but not write to it. They will access the data by using a Spark notebook.
  • The data analysts must have read access to only the dimensional model objects in the data store. They also must have access to create Power BI reports by using the semantic models created by the analytics engineers.
  • The date dimension must be available to all users of the data store.
  • The principle of least privilege must be followed.
  • FabricAdmins: Fabric administrators
  • AnalyticsTeam: All the members of the analytics team
  • DataAnalysts: The data analysts on the analytics team
  • DataScientists: The data scientists on the analytics team
  • DataEngineers: The data engineers on the analytics team
  • AnalyticsEngineers: The analytics engineers on the analytics team

Report Requirements

  • Enables a user to select a product to filter customer survey responses to only those who have purchased that product
  • Displays the average overall satisfaction score of all the surveys submitted during the last 12 months up to a selected date
  • Shows data as soon as the data is updated in the data store
  • Ensures that the report and the semantic model only contain data from the current and previous year
  • Ensures that the report respects any table-level security specified in the source data store
  • Minimizes the execution time of report queries
You need to ensure the data loading activities in the AnalyticsPOC workspace are executed in the appropriate sequence. The solution must meet the technical requirements.

What should you do?

Select an answer above, then submit your vote. Reading or practicing does not cast a vote.

Question 6

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 semantic model named Model1.

You discover that the following query performs slowly against Model1.

1  EVALUATE
2     FILTER (
3       VALUES ( Customer[Customer Name] ),
4       CALCULATE ( COUNTROWS ( 'Order Item' ) ) > 0
5     )
6     ORDER BY Customer[Customer Name]

You need to reduce the execution time of the query without changing the results.

Solution: You replace line 4 by using the following code:

NOT ( ISEMPTY ( CALCULATETABLE ( 'Order Item' ) ) )

Does this meet the goal?

Select an answer above, then submit your vote. Reading or practicing does not cast a vote.