2025 Latest DP-600 dumps Exam Material with 165 Questions
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Microsoft DP-600 Exam Syllabus Topics:
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NEW QUESTION # 25
You have a Fabric workspace named Workspacel that contains a lakehouse named Lakehousel. Lakehousel contains a table named Tablel. Table 1 contains the following data.
You need to perform the following actions:
* Load the data from Table! into a star schema.
* Create a product dimension table named DimProduct and a fact table named FactSales.
Which three columns should you include in DimProduct?
- A. Date, ProductID, andTransactionlD.
- B. ProductColor, ProductID, and ProductName.
- C. ProductID, ProductName, and SalesAmount
- D. ProductName, SalesAmount, andTransactionlD
Answer: B
NEW QUESTION # 26
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.
Answer:
Explanation:
Explanation:
NEW QUESTION # 27
You are creating a semantic model in Microsoft Power Bl Desktop.
You plan to make bulk changes to the model by using the Tabular Model Definition Language (TMDL) extension for Microsoft Visual Studio Code.
You need to save the semantic model to a file.
Which file format should you use?
- A. PBIDS
- B. PBIP
- C. PBIX
- D. PBIT
Answer: C
NEW QUESTION # 28
You have a Fabric warehouse that contains a table named Staging.Sales. Staging.Sales contains the following columns.
You need to write a T-SQL query that will return data for the year 2023 that displays ProductID and ProductName arxl has a summarized Amount that is higher than 10,000. Which query should you use?
- A.
- B.
- C.
- D.
Answer: A
Explanation:
The correct query to use in order to return data for the year 2023 that displays ProductID, ProductName, and has a summarized Amount greater than 10,000 is Option B.
The reason is that it uses the GROUP BY clause to organize the data by ProductID and ProductName and then filters the result using the HAVING clause to only include groups where the sum of Amount is greater than 10,000. Additionally, the DATEPART(YEAR, SaleDate) = '2023' part of the HAVING clause ensures that only records from the year 2023 are included. Reference = For more information, please visit the official documentation on T-SQL queries and the GROUP BY clause at T-SQL GROUP BY.
NEW QUESTION # 29
Case Study 1 - Contoso
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?
- A. Store at the semantic models and reports in Data Lake Gen2 storage.
- B. Store all the semantic models and reports in Microsoft OneDrive.
- C. Modify the settings of the Research division workspaces to use an Azure Repos repository.
- D. Modify the settings of the Research workspaces to use a GitHub repository.
Answer: C
Explanation:
Currently, only Git in Azure Repos is supported.
https://learn.microsoft.com/en-us/fabric/cicd/git-integration/intro-to-git-integration#considerations- and-limitations
NEW QUESTION # 30
You have a Fabric tenant that contains a Microsoft Power Bl report named Report 1.
Report1 is slow to render. You suspect that an inefficient DAX query is being executed.
You need to identify the slowest DAX query, and then review how long the query spends in the formula engine as compared to the storage engine.
Which five actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.
Answer:
Explanation:
NEW QUESTION # 31
You have a Fabric warehouse that contains a table named SalesOrderDetail. SalesOrderDetail contains three columns named OrderQty, ProductID and SalesOrderlD. SalesOrderDetail contains one row per combination of SalesOrderlD and ProductID.
You need to calculate the proportion of the total quantity of each sales order represented by each product within the sales order.
Which T-SQL statement should you run?
- A.
- B.
- C.
- D.
Answer: D
NEW QUESTION # 32
Hotspot Question
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.
Answer:
Explanation:
NEW QUESTION # 33
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 # 34
You have a Fabric tenant.
You plan to create a Fabric notebook that will use Spark DataFrames to generate Microsoft Power Bl visuals.
You run 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 code embeds an existing Power BI report. - No
* The code creates a Power BI report. - No
* The code displays a summary of the DataFrame. - Yes
The code provided seems to be a snippet from a SQL query or script which is neither creating nor embedding a Power BI report directly. It appears to be setting up a DataFrame for use within a larger context, potentially for visualization in Power BI, but the code itself does not perform the creation or embedding of a report. Instead, it's likely part of a data processing step that summarizes data.
References =
* Introduction to DataFrames - Spark SQL
* Power BI and Azure Databricks
NEW QUESTION # 35
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 Bl report named Report1 that uses the model. Report! 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.
- A. automatic aggregation
- B. OneLake integration
- C. query caching
- D. user-defined aggregations
Answer: B,C
Explanation:
User-defined aggregations (A) and query caching (C) are two features that can help reduce query execution time. User-defined aggregations allow precalculation of large datasets, and query caching stores the results of queries temporarily to speed up future queries. References = Microsoft Power BI documentation on performance optimization offers in-depth knowledge on these features.
NEW QUESTION # 36
You have a Fabric tenant that contains a workspace named Workspace^ Workspacel is assigned to a Fabric capacity.
You need to recommend a solution to provide users with the ability to create and publish custom Direct Lake semantic models by using external tools. The solution must follow the principle of least privilege.
Which three actions in the Fabric Admin portal should you include in the recommendation? Each correct answer presents part of the solution.
NOTE: Each correct answer is worth one point.
- A. From the Tenant settings, select Users can edit data models in the Power Bl service.
- B. From the Tenant settings, enable Publish to Web
- C. From the Tenant settings, set Allow XMLA Endpoints and Analyze in Excel with on-premises datasets to Enabled
- D. From the Tenant settings, set Allow Azure Active Directory guest users to access Microsoft Fabric to Enabled
- E. From the Tenant settings, set Users can create Fabric items to Enabled
- F. From the Capacity settings, set XMLA Endpoint to Read Write
Answer: A,C,F
Explanation:
For users to create and publish custom Direct Lake semantic models using external tools, following the principle of least privilege, the actions to be included are enabling XMLA Endpoints (A), editing data models in Power BI service (C), and setting XMLA Endpoint to Read-Write in the capacity settings (D). References = More information can be found in the Admin portal of the Power BI service documentation, detailing tenant and capacity settings.
NEW QUESTION # 37
You have a Fabric tenant that contains a Microsoft Power Bl report named Report 1.
Report1 is slow to render. You suspect that an inefficient DAX query is being executed.
You need to identify the slowest DAX query, and then review how long the query spends in the formula engine as compared to the storage engine.
Which five actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.
Answer:
Explanation:
Explanation:
To identify the slowest DAX query and analyze the time it spends in the formula engine compared to the storage engine, you should perform the following actions in sequence:
* From Performance analyzer, capture a recording.
* View the Server Timings tab.
* Enable Query Timings and Server Timings. Run the query.
* View the Query Timings tab.
* Sort the Duration (ms) column in descending order by DAX query time.
NEW QUESTION # 38
You have a Fabric tenant that contains a workspace named Workspace^ Workspacel is assigned to a Fabric capacity.
You need to recommend a solution to provide users with the ability to create and publish custom Direct Lake semantic models by using external tools. The solution must follow the principle of least privilege.
Which three actions in the Fabric Admin portal should you include in the recommendation? Each correct answer presents part of the solution.
NOTE: Each correct answer is worth one point.
- A. From the Tenant settings, select Users can edit data models in the Power Bl service.
- B. From the Tenant settings, enable Publish to Web
- C. From the Tenant settings, set Allow XMLA Endpoints and Analyze in Excel with on-premises datasets to Enabled
- D. From the Tenant settings, set Allow Azure Active Directory guest users to access Microsoft Fabric to Enabled
- E. From the Tenant settings, set Users can create Fabric items to Enabled
- F. From the Capacity settings, set XMLA Endpoint to Read Write
Answer: A,C,F
Explanation:
For users to create and publish custom Direct Lake semantic models using external tools, following the principle of least privilege, the actions to be included are enabling XMLA Endpoints (A), editing data models in Power BI service (C), and setting XMLA Endpoint to Read-Write in the capacity settings (D). Reference = More information can be found in the Admin portal of the Power BI service documentation, detailing tenant and capacity settings.
NEW QUESTION # 39
You have a Fabric tenant that contains a workspace named Workspace1 and a user named User1. Workspace1 contains a warehouse named DW1.
You share DW1 with User1 and assign User1 the default permissions for DW1.
What can User1 do?
- A. Build reports by using the default dataset.
- B. Connect to DW1 via the TDS (Tabular Data Stream) endpoint.
- C. Read the underlying Parquet files from OneLake.
- D. Read data from the tables in DW1.
Answer: A
NEW QUESTION # 40
You are analyzing customer purchases in a Fabric notebook by using PySpanc You have the following DataFrames:
You need to join the DataFrames on the customer_id column. The solution must minimize data shuffling. You write the following code.
Which code should you run to populate the results DataFrame?
- A.
- B.
- C.
- D.
Answer: C
Explanation:
The correct code to populate the results DataFrame with minimal data shuffling is Option A. Using the broadcast function in PySpark is a way to minimize data movement by broadcasting the smaller DataFrame (customers) to each node in the cluster. This is ideal when one DataFrame is much smaller than the other, as in this case with customers. Reference = You can refer to the official Apache Spark documentation for more details on joins and the broadcast hint.
NEW QUESTION # 41
You have a Fabric warehouse that contains the following data.
The data has the following characteristics:
* Each customer is assigned a unique CustomerlD value.
* Each customer is associated to a single SalesRegion value.
* Each customer is associated to a single CustomerAddress value.
* The Customer table contains 5 million rows.
* All foreign key values are non-null.
You need to create a view to denormalize the data into a customer dimension that contains one row per distinct CustomerlD value. The solution must minimize query processing time and resources.
How should you complete the T-SQL statement? To answer, select the appropriate options in the answer area.
Answer:
Explanation:
Explanation:
NEW QUESTION # 42
You have a Fabric workspace named Workspacel that contains a lakehouse named Lakehousel. Lakehousel contains a table named Tablel. Table 1 contains the following data.
You need to perform the following actions:
* Load the data from Table! into a star schema.
* Create a product dimension table named DimProduct and a fact table named FactSales.
Which three columns should you include in DimProduct?
- A. Date, ProductID, andTransactionlD.
- B. ProductColor, ProductID, and ProductName.
- C. ProductID, ProductName, and SalesAmount
- D. ProductName, SalesAmount, andTransactionlD
Answer: B
Explanation:
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 # 43
You have a Fabric tenant that contains a lakehouse named LH1.
You need to deploy a new semantic model. The solution must meet the following requirements:
* Support complex calculated columns that include aggregate functions, calculated tables, and Multidimensional Expressions (MDX) user hierarchies.
* Minimize page rendering times.
How should you configure the model? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
NEW QUESTION # 44
You have a semantic model named Model 1. Model 1 contains five tables that all use Import mode. Model1 contains a dynamic row-level security (RLS) role named HR. The HR role filters employee data so that HR managers only see the data of the department to which they are assigned.
You publish Model1 to a Fabric tenant and configure RLS role membership. You share the model and related reports to users.
An HR manager reports that the data they see in a report is incomplete.
What should you do to validate the data seen by the HR Manager?
- A. Filter the data in the report to match the intended logic of the filter for the HR department.
- B. Ask the HR manager to open the report in Microsoft Power Bl Desktop.
- C. Select Test as role to view the data as the HR role.
- D. Select Test as role to view the report as the HR manager,
Answer: C
Explanation:
To validate the data seen by the HR manager, you should use the 'Test as role' feature in Power BI service. This allows you to see the data exactly as it would appear for the HR role, considering the dynamic RLS setup. Here is how you would proceed:
Navigate to the Power BI service and locate Model1.
Access the dataset settings for Model1.
Find the security/RLS settings where you configured the roles.
Use the 'Test as role' feature to simulate the report viewing experience as the HR role.
Review the data and the filters applied to ensure that the RLS is functioning correctly.
If discrepancies are found, adjust the RLS expressions or the role membership as needed.
NEW QUESTION # 45
You have a Fabric tenant that contains a warehouse named DW1 and a lakehouse named LH1. DW1 contains a table named Sales.Product. LH1 contains a table named Sales.Orders.
You plan to schedule an automated process that will create a new point-in-time (PIT) table named Sales.
ProductOrder in DW1. Sales.ProductOrder will be built by using the results of a query that will join Sales.
Product and Sales.Orders.
You need to ensure that the types of columns in Sales. ProductOrder match the column types in the source tables. The solution must minimize the number of operations required to create the new table.
Which operation should you use?
- A. CREATE MATERIALIZED VIEW AS SELECT
- B. INSERT INTO
- C. CREATE TABLE AS SELECT (CTAS)
- D. CREATE TABLE AS CLONE OF
Answer: C
NEW QUESTION # 46
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