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Microsoft DP-600 Exam Syllabus Topics:
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NEW QUESTION # 19
What should you recommend using to ingest the customer data into the data store in the AnatyticsPOC workspace?
- A. a Spark notebook
- B. a stored procedure
- C. a pipeline that contains a KQL activity
- D. a dataflow
Answer: D
Explanation:
For ingesting customer data into the data store in the AnalyticsPOC workspace, a dataflow (D) should be recommended. Dataflows are designed within the Power BI service to ingest, cleanse, transform, and load data into the Power BI environment. They allow for the low-code ingestion and transformation of data as needed by Litware's technical requirements. Reference = You can learn more about dataflows and their use in Power BI environments in Microsoft's Power BI documentation.
Topic 1, Litware. Inc.
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 # 20
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.
Answer:
Explanation:
Explanation:
NEW QUESTION # 21
You have a Fabric tenant that contains a warehouse.
You use a dataflow to load a new dataset from OneLake to the warehouse.
You need to add a Power Query step to identify the maximum values for the numeric columns.
Which function should you include in the step?
- A. Table.Range
- B. Table.Max
- C. Table. MaxN
- D. Table.Profile
Answer: B
NEW QUESTION # 22
You have a Fabric warehouse named Warehousel that contains a table named Table! Tablel contains customer data.
You need to implement row-level security (RLS) for Tablel. The solution must ensure that users can see only their respective data.
Which two objects should you create? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
- A. DATABASE ROLE
- B. FUNCTION
- C. CONSTRAINT
- D. STORED PROCEDURE
- E. SECURITY POLICY
Answer: A,E
NEW QUESTION # 23
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?
- A. Yes
- B. No
Answer: B
Explanation:
The df.show() method also does not meet the goal. It is used to show the contents of the DataFrame, not to compute statistical functions. References = The usage of the show() function is documented in the PySpark API documentation.
NEW QUESTION # 24
You have a Fabric tenant that contains a data warehouse named DW1. DW1 contains a table named DimCustomer. DimCustomer contains the fields shown in the following table.
You need to identify duplicate email addresses in DimCustomer. The solution must return a maximum of
1,000 records.
Which four T-SQL statements should you run in sequence? To answer, move the appropriate statements from the list of statements to the answer area and arrange them in the correct order.
Answer:
Explanation:
Explanation:
NEW QUESTION # 25
You are creating a dataflow in Fabric to ingest data from an Azure SQL database by using a T-SQL statement.
You need to ensure that any foldable Power Query transformation steps are processed by the Microsoft SQL Server engine.
How should you complete the code? 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:
NEW QUESTION # 26
Hotspot Question
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.
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:
NEW QUESTION # 27
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. ProductName, SalesAmount, andTransactionlD
- D. ProductID, ProductName, and SalesAmount
Answer: B
NEW QUESTION # 28
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:
NEW QUESTION # 29
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: D
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 # 30
You to need assign permissions for the data store in the AnalyticsPOC workspace. The solution must meet the security requirements.
Which additional permissions should you assign when you share the data store? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
* Data Engineers: Read All SQL analytics endpoint data
* Data Analysts: Read All Apache Spark
* Data Scientists: Read All SQL analytics endpoint data
The permissions for the data store in the AnalyticsPOC workspace should align with the principle of least privilege:
* Data Engineers need read and write access but not to datasets or reports.
* Data Analysts require read access specifically to the dimensional model objects and the ability to create Power BI reports.
* Data Scientists need read access via Spark notebooks. These settings ensure each role has the necessary permissions to fulfill their responsibilities without exceeding their required access level.
NEW QUESTION # 31
You have a Fabric tenant that contains a semantic model. The model uses Direct Lake mode.
You suspect that some DAX queries load unnecessary columns into memory.
You need to identify the frequently used columns that are loaded into memory.
What are two ways to achieve the goal? Each correct answer presents a complete solution.
NOTE: Each correct answer is worth one point.
- A. Use the Vertipaq Analyzer tool.
- B. Query the $system.discovered_STORAGE_TABLE_COLUMN-iN_SEGMeNTS dynamic management view (DMV).
- C. Use the Analyze in Excel feature.
- D. Query the discover_hehory6Rant dynamic management view (DMV).
Answer: A,B
Explanation:
The Vertipaq Analyzer tool (B) and querying the $system.discovered_STORAGE_TABLE_COLUMNS_IN_SEGMENTS dynamic management view (DMV) (C) can help identify which columns are frequently loaded into memory. Both methods provide insights into the storage and retrieval aspects of the semantic model. Reference = The Power BI documentation on Vertipaq Analyzer and DMV queries offers detailed guidance on how to use these tools for performance analysis.
NEW QUESTION # 32
You are creating a dataflow in Fabric to ingest data from an Azure SQL database by using a T-SQL statement.
You need to ensure that any foldable Power Query transformation steps are processed by the Microsoft SQL Server engine.
How should you complete the code? 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:
NEW QUESTION # 33
You have a Fabric tenant that contains a lakehouse named Lakehouse1. Lakehouse1 contains a subfolder named Subfolder1 that contains CSV files. You need to convert the CSV files into the delta format that has V-Order optimization enabled. What should you do from Lakehouse explorer?
- A. Create a new shortcut in the Files section.
- B. Use the Load to Tables feature.
- C. Use the Optimize feature.
- D. Create a new shortcut in the Tables section.
Answer: B
NEW QUESTION # 34
You have a Fabric tenant that contains a complex semantic model. The model is based on a star schema and contains many tables, including a fact table named Sales.
You need to visualize a diagram of the model. The diagram must contain only the Sales table and related tables.
What should you use from Microsoft Power BI Desktop?
- A. DAX query view
- B. data categories
- C. Data view
- D. Model view
Answer: D
Explanation:
The Model view in Microsoft Power BI Desktop allows you to visualize the relationships between tables in a semantic model. It displays a diagram of the data model, where you can focus on specific tables, such as the Sales fact table and its related tables, by arranging or filtering the view. This is the ideal tool for analyzing the structure of a star schema and understanding table relationships.
NEW QUESTION # 35
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.
Which syntax should you use in a notebook to access the Research division data for Productline1?
- A. spark.read.format("delta").load("Files/ResearchProduct")
- B. spark.sql("SELECT * FROM Lakehouse1.productline1.ResearchProduct")
- C. spark.read.format("delta").load("Tables/productline1/ResearchProduct")
- D. spark.read.format("delta").load("Tables/ResearchProduct")
Answer: D
NEW QUESTION # 36
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 # 37
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. query caching
- B. user-defined aggregations
- C. OneLake integration
- D. automatic aggregation
Answer: B,D
NEW QUESTION # 38
You have a Fabric tenant that contains a workspace named Workspace! Workspace1 uses the Pro license mode and contains a semantic model named Model1.
You have an Azure DevOps organization.
You need to enable version control for Workspace1. The solution must ensure that Model 1 is added to the repository.
Which three 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:
We need to enable version control for a Fabric workspace (Workspace1) and ensure that Model1 (a semantic model) is added to an Azure DevOps repository.
Key facts:
Workspace1 is in Pro mode. To enable Git integration, the workspace must be assigned to a Fabric capacity.
Git integration steps in Fabric:
Assign the workspace to a Fabric capacity.
Connect the workspace to a Git provider (e.g., Azure DevOps or GitHub).
Sync the workspace with the repository (to push artifacts like semantic models).
Branch policies and deployment pipelines are not needed for simply enabling version control.
Correct Sequence:
Assign Workspace1 to a Fabric capacity.
Connect Workspace1 to a Git provider.
Sync Workspace1 with the repository.
Final Answer:
Step 1 # Assign Workspace1 to a Fabric capacity
Step 2 # Connect Workspace1 to a Git provider
Step 3 # Sync Workspace1 with the repository
References:
Microsoft Fabric Git integration
Enable Git in Fabric workspace
This ensures Model1 will be version-controlled in the linked DevOps repository.
NEW QUESTION # 39
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. ProductName, SalesAmount, andTransactionlD
- D. ProductID, ProductName, and SalesAmount
Answer: B
Explanation:
Step 1 - Understanding star schema design
Fact tables # contain transactional, numeric, and foreign key columns (e.g., TransactionID, Date, SalesAmount, ProductID).
Dimension tables # contain descriptive attributes and business keys (e.g., ProductID, ProductName, ProductColor).
Step 2 - Column classification
TransactionID # Fact (surrogate key for the fact table).
Date # Goes into a Date Dimension (not into DimProduct).
ProductID # Business key # belongs in DimProduct.
ProductColor # Descriptive attribute # belongs in DimProduct.
ProductName # Descriptive attribute # belongs in DimProduct.
SalesAmount # Fact (measure, belongs in FactSales).
Step 3 - Select DimProduct columns
So DimProduct should include:
ProductID
ProductName
ProductColor
That matches option A.
NEW QUESTION # 40
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, set Allow Azure Active Directory guest users to access Microsoft Fabric to Enabled
- B. From the Capacity settings, set XMLA Endpoint to Read Write
- C. From the Tenant settings, set Allow XMLA Endpoints and Analyze in Excel with on-premises datasets to Enabled
- D. From the Tenant settings, enable Publish to Web
- E. From the Tenant settings, select Users can edit data models in the Power Bl service.
- F. From the Tenant settings, set Users can create Fabric items to Enabled
Answer: B,C,D
NEW QUESTION # 41
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?
- A. Split column
- B. Unpivot other columns
- C. Unpivot columns
- D. Group by
- E. Remove other columns
Answer: B
Explanation:
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.
NEW QUESTION # 42
You to need assign permissions for the data store in the AnalyticsPOC workspace. The solution must meet the security requirements.
Which additional permissions should you assign when you share the data store? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
* Data Engineers: Read All SQL analytics endpoint data
* Data Analysts: Read All Apache Spark
* Data Scientists: Read All SQL analytics endpoint data
The permissions for the data store in the AnalyticsPOC workspace should align with the principle of least privilege:
* Data Engineers need read and write access but not to datasets or reports.
* Data Analysts require read access specifically to the dimensional model objects and the ability to create Power BI reports.
* Data Scientists need read access via Spark notebooks. These settings ensure each role has the necessary permissions to fulfill their responsibilities without exceeding their required access level.
NEW QUESTION # 43
......
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