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  • Aug-2026 Realistic DP-600 Exam Dumps with Accurate & Updated Questions [Q14-Q33]

Aug-2026 Realistic DP-600 Exam Dumps with Accurate & Updated Questions [Q14-Q33]

Posted on August 21, 2026 By freedumps No Comments on Aug-2026 Realistic DP-600 Exam Dumps with Accurate & Updated Questions [Q14-Q33]
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Aug-2026 Realistic DP-600 Exam Dumps with Accurate & Updated Questions

DP-600 Exam Dumps – PDF Questions and Testing Engine

Q14. You have source data in a folder on a local computer.
You need to create a solution that will use Fabric to populate a data store. The solution must meet the following requirements:
Support the use of dataflows to load and append data to the data store.
Ensure that Delta tables are V-Order optimized and compacted automatically.
Which type of data store should you use?

 
 
 
 
To meet the requirements of supporting dataflows to load and append data to the data store while ensuring that Delta tables are V-Order optimized and compacted automatically, you should use a lakehouse in Fabric as your solution.

Q15. Which of the following features in the data profiling tools of Microsoft Fabric’s Power Query Editor provides a set of visuals showcasing the frequency and distribution of the values in each column, sorted in descending order from the value with the highest frequency?

 
 
 
 
Option A is correct because The column distribution feature provides visuals underneath the names of the columns showcasing the frequency and distribution of the values. The data in these visualizations is sorted in descending order from the value with the highest frequency.
Reference:
https://learn.microsoft.com/en-us/power-query/data-profiling-tools

Q16. You need to resolve the issue with the pricing group classification.
How should you complete the T-SQL statement? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Q17. You have a Fabric tenant that contains a workspace named Workspace1 and a user named User1. User1 is assigned the Contributor role for Workspace1.
You plan to configure Workspace1 to use an Azure DevOps repository for version control.
You need to ensure that User1 can commit items to the repository.
Which two settings should you enable for User1? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.

 
 
 
 

Q18. You have a Fabric tenant.
You need to configure OneLake security for users shown in the following table.

The solution must follow the principle of least privilege.
Which permission should you assign to each user? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Q19. What should you recommend using to ingest the customer data into the data store in the AnatyticsPOC workspace?

 
 
 
 

Q20. You have an Amazon Web Services (AWS) subscription that contains an Amazon Simple Storage Service (Amazon S3) bucket named bucket1.
You have a Fabric tenant that contains a lakehouse named LH1.
In LH1, you plan to create a OneLake shortcut to bucket1.
You need to configure authentication for the connection.
Which two values should you provide? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.

 
 
 
 
 
https://learn.microsoft.com/sl-si/fabric/onelake/create-s3-compatible-shortcut

Q21. Hotspot Question
You have a Fabric tenant that contains a workspace named Workspace1. Workspace1 contains a lakehouse named LH1 and a warehouse named DW1. LH1 contains a table named signindata that is in the dbo schema.
You need to create a stored procedure in DW1 that deduplicates the data in the signindata table.
How should you complete the T-SQL statement? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Q22. You have a Fabric tenant that contains a warehouse named WH1. You run the following T-SQL query against WH1.

For each of the following statements, select Yes if the statement is true. Otherwise, select No. NOTE: Each correct selection is worth one point.


Explanation:

Q23. You are implementing two dimension tables named Customers and Products in a Fabric warehouse.
You need to use slowly changing dimension (SCO) to manage the versioning of data. The solution must meet the requirements shown in the following table.

Which type of SCD should you use for each table? To answer, drag the appropriate SCD types to the correct tables. Each SCD type may be used once, more than once, or not at all. You may need to drag the split bar between panes o r scroll to view content.
NOTE: Each correct selection is worth one point.


Explanation:

For the Customers table, where the requirement is to create a new version of the row, you would use:
Type 2 SCD: This type allows for the creation of a new record each time a change occurs, preserving the history of changes over time.
For the Products table, where the requirement is to overwrite the existing value in the latest row, you would use:
Type 1 SCD: This type updates the record directly, without preserving historical data.

Q24. What are the two limitations of the Visual Query Editor? (Select 2)

 
 
 
 
Option A is correct because Visual Query Editor only supports Data Query Language statements which are read-only SELECT statements.
Option C is correct because Visual Query Editor does not support SQL queries having an Order By clause. We can arrange the data in ascending or descending order.
Reference:
https://learn.microsoft.com/en-us/fabric/data-warehouse/visual-query-editor

Q25. You have a Fabric workspace named Workspace1 that contains a data flow named Dataflow1. Dataflow1 contains a query that returns the data shown in the following exhibit.

You need to transform the date columns into attribute-value pairs, where columns become rows.
You select the VendorlD column.
Which transformation should you select from the context menu of the VendorlD column?

 
 
 
 
 
The transformation you should select from the context menu of the VendorID column to transform the date columns into attribute-value pairs, where columns become rows, is Unpivot columns (B). This transformation will turn the selected columns into rows with two new columns, one for the attribute (the original column names) and one for the value (the data from the cells). References = Techniques for unpivoting columns are covered in the Power Query documentation, which explains how to use the transformation in data modeling.

Q26. You have a Fabric warehouse that contains a table named Sales.Orders. Sales.Orders contains the following columns.

You need to write a T-SQL query that will return the following columns.

How should you complete the code? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.


Explanation:

For the PeriodDate that returns the first day of the month for OrderDate, you should use DATEFROMPARTS as it allows you to construct a date from its individual components (year, month, day).
For the DayName that returns the name of the day for OrderDate, you should use DATENAME with the weekday date part to get the full name of the weekday.
The complete SQL query should look like this:
SELECT OrderID, CustomerID,
DATEFROMPARTS(YEAR(OrderDate), MONTH(OrderDate), 1) AS PeriodDate,
DATENAME(weekday, OrderDate) AS DayName
FROM Sales.Orders
Select DATEFROMPARTS for the PeriodDate and weekday for the DayName in the answer area.

Q27. You have a Fabric warehouse that contains two tables named DimDate and Trips.
DimDate contains the following fields.

Trips contains the following fields.

You need to compare the average miles per trip for statutory holidays versus non-statutory holidays.
How should you complete the T-SQL statement? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.


Explanation:

Comprehensive Detailed Explanation
Step 1: Requirement
We need to compare the average miles per trip for:
Statutory holidays (when IsHoliday = 1 )
Non-statutory holidays (when IsHoliday = 0 )
Step 2: Formula for average miles per trip
Average miles per trip = total miles ÷ number of trips
Total miles # SUM(t.tripDistance)
Number of trips # COUNT(t.tripID)
So the calculation is:
( SUM (t.tripDistance) / COUNT (t.tripID)) AS MilesPerTrip
Step 3: Grouping
We need a comparison by holiday status .
So we must group the results by:
GROUP BY d.IsHoliday
This ensures we get two rows: one for IsHoliday = 1 and one for IsHoliday = 0 .
Step 4: Final Query
SELECT
d.IsHoliday,
( SUM (t.tripDistance) / COUNT (t.tripID)) AS MilesPerTrip
FROM DimDate d
INNER JOIN Trips t ON d.DateID = t.DateID
GROUP BY d.IsHoliday;
Why This is Correct
The formula ensures average miles per trip .
Grouping ensures comparison between holidays vs non-holidays .
Efficient aggregation, minimal computation.
References
Aggregate functions in T-SQL
GROUP BY clause

Q28. You have a Fabric tenant that contains customer churn data stored as Parquet files in OneLake. The data contains details about customer demographics and product usage.
You create a Fabric notebook to read the data into a Spark DataFrame. You then create column charts in the notebook that show the distribution of retained customers as compared to lost customers based on geography, the number of products purchased, age. and customer tenure.
Which type of analytics are you performing?

 
 
 
 
The charts in the Fabric notebook are summarizing customer churn data (retained vs lost customers) across dimensions such as geography, products purchased, age, and tenure. This is describing “what has happened” in the data.
Descriptive analytics summarizes and visualizes historical data.
Diagnostic analytics explains “why” events occurred.
Predictive analytics forecasts future outcomes.
Prescriptive analytics recommends actions.
Since only distributions are being shown, this is descriptive analytics.
References:
Microsoft Learn – Types of Analytics in Fabric
Descriptive Analytics concepts

Q29. You have two Microsoft Power Bl queries named Employee and Retired Roles.
You need to merge the Employee query with the Retired Roles query. The solution must ensure that rows in the Employee query that match the Retired Roles query are removed.
Which column and Join Kind should you use in Power Query Editor? To answer, select the appropriate options in the answer area.
NOTE: Each correct answer is worth one point


Explanation:

Q30. You have a Fabric tenant that contains a warehouse.
A user discovers that a report that usually takes two minutes to render has been running for 45 minutes and has still not rendered.
You need to identify what is preventing the report query from completing.
Which dynamic management view (DMV) should you use?

 
 
 
 
The correct DMV to identify what is preventing the report query from completing is sys.dm_pdw_exec_requests (D). This DMV is specific to Microsoft Analytics Platform System (previously known as SQL Data Warehouse), which is the environment assumed to be used here. It provides information about all queries and load commands currently running or that have recently run. References = You can find more about DMVs in the Microsoft documentation for Analytics Platform System.

Q31. You have a Microsoft Power Bl semantic model.
You plan to implement calculation groups.
You need to create a calculation item that will change the context from the selected date to month-to-date (MTD).
How should you complete the DAX expression? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.


Explanation:

To create a calculation item that changes the context from the selected date to month-to-date (MTD), the appropriate DAX expression involves using the CALCULATE function to alter the filter context and the DATESMTD function to specify the month-to-date context.
The correct completion for the DAX expression would be:
* In the first dropdown, select CALCULATE.
* In the second dropdown, select SELECTEDMEASURE.
This would create a DAX expression in the form:
CALCULATE(
SELECTEDMEASURE(),
DATESMTD(‘Date'[DateColumn])
)

Q32. 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.show()
Does this meet the goal?

 
 

Q33. Hotspot Question
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.


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Microsoft DP-600 Exam Syllabus Topics:

Topic Details
Topic 1
  • Prepare data: This section of the exam measures the skills of engineers and covers essential data preparation tasks. It includes establishing data connections and discovering sources through tools like the OneLake data hub and the real-time hub. Candidates must demonstrate knowledge of selecting the appropriate storage type—lakehouse, warehouse, or eventhouse—depending on the use case. It also includes implementing OneLake integrations with Eventhouse and semantic models. The transformation part involves creating views, stored procedures, and functions, as well as enriching, merging, denormalizing, and aggregating data. Engineers are also expected to handle data quality issues like duplicates, missing values, and nulls, along with converting data types and filtering. Furthermore, querying and analyzing data using tools like SQL, KQL, and the Visual Query Editor is tested in this domain.
Topic 2
  • Maintain a data analytics solution: This section of the exam measures the skills of administrators and covers tasks related to enforcing security and managing the Power BI environment. It involves setting up access controls at both workspace and item levels, ensuring appropriate permissions for users and groups. Row-level, column-level, object-level, and file-level access controls are also included, alongside the application of sensitivity labels to classify data securely. This section also tests the ability to endorse Power BI items for organizational use and oversee the complete development lifecycle of analytics assets by configuring version control, managing Power BI Desktop projects, setting up deployment pipelines, assessing downstream impacts from various data assets, and handling semantic model deployments using XMLA endpoint. Reusable asset management is also a part of this domain.
Topic 3
  • Implement and manage semantic models: This section of the exam measures the skills of architects and focuses on designing and optimizing semantic models to support enterprise-scale analytics. It evaluates understanding of storage modes and implementing star schemas and complex relationships, such as bridge tables and many-to-many joins. Architects must write DAX-based calculations using variables, iterators, and filtering techniques. The use of calculation groups, dynamic format strings, and field parameters is included. The section also includes configuring large semantic models and designing composite models. For optimization, candidates are expected to improve report visual and DAX performance, configure Direct Lake behaviors, and implement incremental refresh strategies effectively.

 

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