Mar 11, 2026 Step by Step Guide to Prepare for DP-600 Exam BrainDumps
Microsoft Certified DP-600 Real Exam Questions and Answers FREE Updated on 2026
Microsoft DP-600 Exam Syllabus Topics:
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NEW QUESTION # 30
You have an Amazon Web Services (AWS) subscription that contains an Amazon Simple Storage Service (Amazon S3) bucket named bucketl.
You have a Fabric tenant that contains a lakehouse named LH1.
In LH1, you plan to create a OneLake shortcut to bucketl.
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.
- A. the shared access signature (SAS) token
- B. the access key ID
- C. the access ID
- D. the secret access key
- E. the certificate thumbprint
Answer: B,D
Explanation:
When creating a OneLake shortcut to Amazon S3, authentication uses the standard AWS access key ID + secret access key pair.
SAS token is for Azure Storage, not AWS.
Certificate thumbprint is not used for S3.
Access ID is incorrect naming; the correct term is access key ID.
Correct answers: B and D.
Reference: Create shortcuts to Amazon S3 in OneLake
NEW QUESTION # 31
You have a Fabric tenant that uses a Microsoft tower Bl Premium capacity. You need to enable scale-out for a semantic model. What should you do first?
- A. At the semantic model level, set Large dataset storage format to Off.
- B. At the tenant level, set Data Activator to Enabled.
- C. At the semantic model level, set Large dataset storage format to On.
- D. At the tenant level, set Create and use Metrics to Enabled.
Answer: C
Explanation:
To enable scale-out for a semantic model, you should first set Large dataset storage format to On (C) at the semantic model level. This configuration is necessary to handle larger datasets effectively in a scaled-out environment. References = Guidance on configuring large dataset storage formats for scale-out is available in the Power BI documentation.
NEW QUESTION # 32
Which syntax should you use in a notebook to access the Research division data for Productlinel?
- A.
- B.
- C.
- D.
Answer: C
NEW QUESTION # 33
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
Answer:
Explanation:
Explanation:
NEW QUESTION # 34
You have a Fabric tenant.
You are creating a Fabric Data Factory pipeline.
You have a stored procedure that returns the number of active customers and their average sales for the current month.
You need to add an activity that will execute the stored procedure in a warehouse. The returned values must be available to the downstream activities of the pipeline.
Which type of activity should you add?
- A. Append variable
- B. Lookup
- C. Switch
- D. KQL
Answer: B
Explanation:
Lookup activity reads and returns the content of a configuration file or table. It also returns the result of executing a query or stored procedure. The output can be a singleton value or an array of attributes, which can be consumed in a subsequent copy, transformation, or control flow activities like ForEach activity.
https://learn.microsoft.com/en-us/azure/data-factory/control-flow-lookup-activity
NEW QUESTION # 35
You have a Fabric tenant named Tenant1 that contains a lakehouse named Lakehouse1.
You need to add data to Lakehouse1 from a CSV file in an Azure Storage account outside of Fabric. The solution must minimize development effort.
What should you use to add the data?
- A. Dataflow Gen2
- B. shortcut
- C. copy job
- D. pipeline
Answer: B
NEW QUESTION # 36
You have a Microsoft Power Bl project that contains a file named definition.pbir. definition.pbir contains the following JSON.
For each of the following statements, select Yes if the statement is true. Otherwise, select No. NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
NEW QUESTION # 37
You have a Fabric tenant that contains a lakehouse named LH1.
You create new tables in LH1.
You need to ensure that the tables are added automatically to the default semantic model.
What should you do?
- A. From the settings pane of LH1, enable Sync the default Power BI semantic model.
- B. Disable Query Caching for the default semantic model.
- C. From the Endorsement and discovery settings of LH1, select Make discoverable.
- D. Enable Refresh for the default semantic model.
Answer: A
Explanation:
To add objects such as tables or views to the default Power BI semantic model, you have options:
1. Manually enable the Sync the default Power BI semantic model setting that will automatically add objects to the semantic model. For more information, see Sync the default Power BI semantic model.
2. Manually add objects to the semantic model.
https://learn.microsoft.com/en-us/fabric/data-warehouse/default-power-bi-semantic-mode
NEW QUESTION # 38
You have a Fabric workspace that uses the default Spark starter pool and runtime version 1,2.
You plan to read a CSV file named Sales.raw.csv in a lakehouse, select columns, and save the data as a Delta table to the managed area of the lakehouse. Sales_raw.csv contains 12 columns.
You have the following code.
For each of the following statements, select Yes if the statement is true. Otherwise, select No. NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
* The Spark engine will read only the 'SalesOrderNumber', 'OrderDate', 'CustomerName', 'UnitPrice' columns from Sales_raw.csv. - Yes
* Removing the partition will reduce the execution time of the query. - No
* Adding inferSchema='true' to the options will increase the execution time of the query. - Yes The code specifies the selection of certain columns, which means only those columns will be read into the DataFrame. Partitions in Spark are a way to optimize the execution of queries by organizing the data into parts that can be processed in parallel. Removing the partition could potentially increase the execution time because Spark would no longer be able to process the data in parallel efficiently. The inferSchema option allows Spark to automatically detect the column data types, which can increase the execution time of the initial read operation because it requires Spark to read through the data to infer the schema.
NEW QUESTION # 39
Hotspot Question
You have a Fabric warehouse that contains a table named Sales.Products. Sales.Products 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 answer is worth one point.
Answer:
Explanation:
NEW QUESTION # 40
You have a Microsoft Power Bl report named Report1 that uses a Fabric semantic model.
Users discover that Report1 renders slowly.
You open Performance analyzer and identify that a visual named Orders By Date is the slowest to render. The duration breakdown for Orders By Date is shown in the following table.
What will provide the greatest reduction in the rendering duration of Report1?
- A. Reduce the number of visuals in Report1.
- B. Change the visual type of Orders By Dale.
- C. Optimize the DAX query of Orders By Date by using DAX Studio.
- D. Enable automatic page refresh.
Answer: A
Explanation:
Based on the duration breakdown provided, the major contributor to the rendering duration is categorized as
"Other," which is significantly higher than DAX Query and Visual display times. This suggests that the issue is less likely with the DAX calculation or visual rendering times and more likely related to model performance or the complexity of the visual. However, of the options provided, optimizing the DAX query can be a crucial step, even if "Other" factors are dominant. Using DAX Studio, you can analyze and optimize the DAX queries that power your visuals for performance improvements. Here's how you might proceed:
* Open DAX Studio and connect it to your Power BI report.
* Capture the DAX query generated by the Orders By Date visual.
* Use the Performance Analyzer feature within DAX Studio to analyze the query.
* Look for inefficiencies or long-running operations.
* Optimize the DAX query by simplifying measures, removing unnecessary calculations, or improving iterator functions.
* Test the optimized query to ensure it reduces the overall duration.
References: The use of DAX Studio for query optimization is a common best practice for improving Power BI report performance as outlined in the Power BI documentation.
NEW QUESTION # 41
You have a data warehouse that contains a table named Stage. Customers. Stage-Customers contains all the customer record updates from a customer relationship management (CRM) system. There can be multiple updates per customer You need to write a T-SQL query that will return the customer ID, name, postal code, and the last updated time of the most recent row for each customer ID.
How should you complete the code? To answer, select the appropriate options in the answer area, NOTE Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
* In the ROW_NUMBER() function, choose OVER (PARTITION BY CustomerID ORDER BY LastUpdated DESC).
* In the WHERE clause, choose WHERE X = 1.
To select the most recent row for each customer ID, you use the ROW_NUMBER() window function partitioned by CustomerID and ordered by LastUpdated in descending order. This will assign a row number of 1 to the most recent update for each customer. By selecting rows where the row number (X) is 1, you get the latest update per customer.
References =
* Use the OVER clause to aggregate data per partition
* Use window functions
NEW QUESTION # 42
You have a Fabric workspace named Workspace 1 that contains a dataflow named Dataflow1. Dataflow1 has a query that returns 2.000 rows. You view the query in Power Query as shown in the following exhibit.
What can you identify about the pickupLongitude column?
- A. The column has missing values.
- B. All the table rows are profiled.
- C. The column has duplicate values.
- D. There are 935 values that occur only once.
Answer: C
Explanation:
The pickupLongitude column has duplicate values. This can be inferred because the 'Distinct count' is 935 while the 'Count' is 1000, indicating that there are repeated values within the column. References = Microsoft Power BI documentation on data profiling could provide further insights into understanding and interpreting column statistics like these.
NEW QUESTION # 43
You have a Fabric workspace named Workspace1.
You need to create a semantic model named Model1 and publish Model1 to Workspace1. The solution must meet the following requirements:
Can revert to previous versions of Model1 as required.
Identifies differences between saved versions of Model1.
Uses Microsoft Power BI Desktop to publish to Workspace1.
Can edit item definition files by using Microsoft Visual Studio Code.
Which two actions should you perform? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
- A. Save Model1 in Power BI Desktop as a PBIP file.
- B. Enable Git integration for Workspace1.
- C. Enable users to edit data models in the Power BI service.
- D. Save Model1 in Power BI Desktop as a PBIX file.
Answer: A,B
Explanation:
Requirements:
Revert to previous versions of Model1.
Identify differences between saved versions.
Publish from Power BI Desktop.
Edit item definition files with Visual Studio Code.
Analysis:
To meet version control and difference tracking requirements, you must use Git integration in the Fabric workspace.
To enable editing with VS Code, you must use PBIP format (Power BI Project), which saves report/model as a folder with definition JSON files (not PBIX).
PBIX is a single binary file, not suitable for Git versioning or editing definition files.
"Enable users to edit in service" is irrelevant.
Correct Answers:
A). Enable Git integration for Workspace1.
C). Save Model1 in Power BI Desktop as a PBIP file.
NEW QUESTION # 44
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?
- A. a lakehouse
- B. a warehouse
- C. a KQL database
- D. an Azure SQL database
Answer: A
Explanation:
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.
NEW QUESTION # 45
You have source data in a CSV file that has the following fields:
* SalesTra nsactionl D
* SaleDate
* CustomerCode
* CustomerName
* CustomerAddress
* ProductCode
* ProductName
* Quantity
* UnitPrice
You plan to implement a star schema for the tables in WH1. Thedimension tables in WH1 will implement Type 2 slowly changing dimension (SCD) logic.
You need to design the tables that will be used for sales transaction analysis and load the source data.
Which type of target table should you specify for the CustomerName, CustomerCode, and SaleDate fields?
To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
We are designing a star schema for sales transactions. In a star schema:
Dimension tables contain descriptive attributes (e.g., Customer, Product, Date).
Fact tables contain measurable, quantitative data (e.g., SalesTransactionID, Quantity, UnitPrice).
Factless fact tables capture events without numeric measures.
Junk dimensions store miscellaneous low-cardinality attributes.
The requirement says dimension tables will use Type 2 Slowly Changing Dimensions (SCD) # so Customer and Date attributes belong to Dimension tables.
Field-by-field classification
CustomerCode
A business key (natural key) for the customer dimension.
Belongs in the Customer Dimension table.
The answer: Dimension.
CustomerName
A descriptive attribute of the customer.
Belongs in the Customer Dimension table.
The answer: Dimension.
SaleDate
A time attribute used for analysis.
Belongs in the Date Dimension table.
The answer: Dimension.
Final Answer:
CustomerCode # Dimension
CustomerName # Dimension
SaleDate # Dimension
References:
Star schema design for Power BI / Fabric
Slowly Changing Dimensions in data warehousing
# This follows Kimball's star schema best practices.
NEW QUESTION # 46
Hotspot Question
You have a Fabric workspace that contains a warehouse named DW1. DW1 contains the following tables and columns.
You need to summarize order quantities by year and product. The solution must include the yearly sum of order quantities for all the products in each row.
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.
Answer:
Explanation:
NEW QUESTION # 47
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.
Answer:
Explanation:
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.
NEW QUESTION # 48
Your company has a finance department.
You have a Fabric tenant, an Azure Storage account named storagel, and a Microsoft Entra group named Groupl. Groupl contains the users in the finance department.
You need to create a new workspace named Workspacel in the tenant. The solution must meet the following requirements:
* Ensure that the finance department users can create and edit items in Workspace"!.
* Ensure that Workspacel can securely access storagel to read and write data.
* Ensure that you are the only admin of Workspacel.
* Minimize administrative effort.
You create Workspacel.
Which two actions should you perform next? Each correct answer presents part of the solution. NOTE: Each correct selection is worth one point.
- A. Assign the Admin role to yourself.
- B. Assign the Contributor role to each finance department user.
- C. Create a workspace identity.
- D. Assign the Contributor role to Groupl.
Answer: C,D
Explanation:
Finance department users can create and edit items in Workspace1 #
The correct role is Contributor.
To minimize effort, assign this role to the Microsoft Entra group (Group1) instead of assigning it to each user individually.
So answer A is correct, not B.
Workspace1 can securely access storagel (Azure Storage) to read and write data # To connect a Fabric workspace to external resources securely, you use a Workspace identity (a managed identity for the workspace).
This allows Fabric items to authenticate to Azure Storage without embedding credentials.
So answer D is correct.
You are the only admin of Workspace1 #
By default, the workspace creator (you) is the admin. You do not need to explicitly reassign the admin role to yourself (so C is unnecessary).
Minimize administrative effort #
Assigning Contributor role to the group (A) is minimal effort compared to assigning it individually to each user (B).
Final Answer:
A). Assign the Contributor role to Group1
D). Create a workspace identity
References:
Workspace roles in Microsoft Fabric
Workspace identity for secure data access
Topic 2, Litware. Inc. Case Study
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
litware has been using a Microsoft Power Bl tenant for three years. Litware has NOT enabled any Fabric capacities and features.
Fabric Environment
Litware has data that must be analyzed as shown in the following table.
The Product data contains a single table and the following columns.
The customer satisfaction data contains the following tables:
* Survey
* Question
* Response
For each survey submitted, the following occurs:
* One row is added to the Survey table.
* One row is added to the Response table for each question in the survey.
The Question table contains the text of each survey question. The third question in each survey response is an overall satisfaction score. Customers can submit a survey after each purchase.
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.
Planned Changes
Litware plans to enable Fabric features in the existing tenant. The analytics team will create a new data store as a proof of concept (PoC). The remaining Litware users will only get access to the Fabric features once the PoC is complete. The PoC will be completed by using a Fabric trial capacity.
The following three workspaces will be created:
* 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 The following will be created in the AnalyticsPOC workspace:
* A data store (type to be decided)
* A custom semantic model
* A default semantic model
* Interactive reports
The data engineers will create data pipelines to load data to OneLake either hourly or daily depending on the data source. The analytics engineers will create processes to ingest transform, and load the data to the data store in the AnalyticsPOC workspace daily. Whenever possible, the data engineers will use low-code tools for data ingestion. The choice of which data cleansing and transformation tools to use will be at the data engineers' discretion.
All the semantic models and reports in the Analytics POC workspace will use the data store as the sole data source.
Technical Requirements
The data store must support the following:
* Read access by using T-SQL or Python
* Semi-structured and unstructured data
* Row-level security (RLS) for users executing T-SQL queries
Files loaded by the data engineers to OneLake will be stored in the Parquet format and will meet Delta Lake specifications.
Data will be loaded without transformation in one area of the AnalyticsPOC data store. The data will then be cleansed, merged, and transformed into a dimensional model.
The data load process must ensure that the raw and cleansed data is updated completely before populating the dimensional model.
The dimensional model must contain a date dimension. There is no existing data source for the date dimension. The Litware fiscal year matches the calendar year. The date dimension must always contain dates from 2010 through the end of the current year.
The product pricing group logic must be maintained by the analytics engineers in a single location. The pricing group data must be made available in the data store for T-SQL queries and in the default semantic model. The following logic must be used:
* 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 pnces that are greater than 1,000 are in the high pricing group.
Security Requirements
Only Fabric administrators and the analytics team must be able to see the Fabric items created as part of the PoC. Litware identifies the following security requirements for the Fabric items in the AnalyticsPOC workspace:
* Fabric administrators will be the workspace administrators.
* The data engineers must be able to read from and write to 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 Bl 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.
Both the default and custom semantic models must include only tables or views from the dimensional model in the data store. Litware already has the following Microsoft Entra security groups:
* 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
* Data Engineers: The data engineers on the analytics team
* Analytics Engineers: The analytics engineers on the analytics team
Report Requirements
The data analysis must create a customer satisfaction report that meets the following 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
NEW QUESTION # 49
You are analyzing the data in a Fabric notebook.
You have a Spark DataFrame assigned to a variable named df.
You need to use the Chart view in the notebook to explore the data manually.
Which function should you run to make the data available in the Chart view?
- A. show
- B. write
- C. display
- D. displayHTML
Answer: C
NEW QUESTION # 50
Hotspot Question
You have a Fabric tenant that contains a workspace named Enterprise. Enterprise contains a semantic model named Model1. Model1 contains a date parameter named Date1 that was created in Power Query.
You build a deployment pipeline named Enterprise Data that includes two stages named Development and Test. You assign the Enterprise workspace to the Development stage.
You need to perform the following actions:
- Create a workspace named Enterprise [Test] and assign the workspace
to the Test stage.
- Configure a rule that will modify the value of Date1 when changes are deployed to the Test stage.
Which two settings should you use? To answer, select the appropriate settings in the answer area.
NOTE: Each correct answer is worth one point.
Answer:
Explanation:
NEW QUESTION # 51
You have a Fabric tenant that contains a lakehouse named lakehouse1. Lakehouse1 contains an unpartitioned table named Table1.
You plan to copy data to Table1 and partition the table based on a date column in the source data.
You create a Copy activity to copy the data to Table1.
You need to specify the partition column in the Destination settings of the Copy activity.
What should you do first?
- A. From the Destination tab, set Mode to Append.
- B. From the Source tab, select Enable partition discovery
- C. From the Destination tab, set Mode to Overwrite.
- D. From the Destination tab, select the partition column,
Answer: C
Explanation:
Before specifying the partition column in the Destination settings of the Copy activity, you should set Mode to Append (A). This will allow the Copy activity to add data to the table while taking the partition column into account. References = The configuration options for Copy activities and partitioning in Azure Data Factory, which are applicable to Fabric dataflows, are outlined in the official Azure Data Factory documentation.
NEW QUESTION # 52
You have a Fabric tenant that contains a semantic model. The model contains data about retail stores.
You need to write a DAX query that will be executed by using the XMLA endpoint The query must return a table of stores that have opened since December 1,2023.
How should you complete the DAX expression? To answer, drag the appropriate values to the correct targets.
Each value may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
The correct order for the DAX expression would be:
* DEFINE VAR _SalesSince = DATE ( 2023, 12, 01 )
* EVALUATE
* FILTER (
* SUMMARIZE ( Store, Store[Name], Store[OpenDate] ),
* Store[OpenDate] >= _SalesSince )
In this DAX query, you're defining a variable _SalesSince to hold the date from which you want to filter the stores. EVALUATE starts the definition of the query. The FILTER function is used to return a table that filters another table or expression. SUMMARIZE creates a summary table for the stores, including the Store[Name] and Store[OpenDate] columns, and the filter expression Store[OpenDate] >= _SalesSince ensures only stores opened on or after December 1, 2023, are included in the results.
References =
* DAX FILTER Function
* DAX SUMMARIZE Function
NEW QUESTION # 53
......
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DP-600 Ultimate Study Guide: https://drive.google.com/open?id=1j93AGdOalY7yH3ZQnKfy7yvbLSuhwJhd
