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Exceptional Practice To Implementing Analytics Solutions Using Microsoft Fabric Pass the First Time
Microsoft DP-600 Exam Syllabus Topics:
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NEW QUESTION # 51
You have a Fabric tenant that contains the workspaces shown in the following table.
You have a deployment pipeline named Pipeline1 that deploys items from Workspace_DEV to Workspace_TEST. In Pipeline1, all items that have matching names are paired.
You deploy the contents of Workspace_DEV to Workspace_TEST by using Pipeline1.
What will the contents of Workspace_TEST be once the deployment is complete?
- A. Lakehouse1
Notebook1
Pipeline1
SemanticModel1 - B. Lakehouse2
Notebook2
SemanticModel1 - C. Lakehouse1
Lakehouse2
Notebook1
Notebook2
Pipeline1
SemanticModel1 - D. Lakehouse2
Notebook2
Pipeline1
SemanticModel1
Answer: C
NEW QUESTION # 52
You have a Fabric warehouse named Warehouse1 that contains a table named dbo.Product. dbo.Product contains the following columns.
You need to use a T-SQL query to add a column named PriceRange to dbo.Product. The column must categorize each product based on UnitPrice. The solution must meet the following requirements:
* If UnitPrice is 0, PriceRange is "Not for resale".
* If UnitPrice is less than 50, PriceRange is "Under $50".
* If UnitPrice is between 50 and 250, PriceRange is "Under $250".
* In all other instances, PriceRange is "$250+".
How should you complete the query? 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:
NEW QUESTION # 53
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.
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 # 54
You have a Fabric tenant that contains the workspaces shown in the following table.
You have a deployment pipeline named Pipeline1 that deploys items from Workspace_DEV to Workspace_TEST. In Pipeline1, all items that have matching names are paired.
You deploy the contents of Workspace_DEV to Workspace_TEST by using Pipeline1.
What will the contents of Workspace_TEST be once the deployment is complete?
- A. Lakehouse1
Notebook1
Pipeline1
SemanticModel1 - B. Lakehouse1
Lakehouse2
Notebook1
Notebook2
Pipeline1
SemanticModel1 - C. Lakehouse2
Notebook2
SemanticModel1 - D. Lakehouse2
Notebook2
Pipeline1
SemanticModel1
Answer: A
Explanation:
Workspace_DEV contents:
Lakehouse1, Notebook1, Pipeline1, SemanticModel1
Workspace_TEST contents (before deployment):
Lakehouse2, Notebook2, SemanticModel1
After deployment:
SemanticModel1 # same name, so it will be paired and overwritten with the DEV version.
Lakehouse1 and Notebook1 # new items, so they will be added to TEST.
Lakehouse2 and Notebook2 # remain because they don't conflict in name.
Pipeline1 # new item, so it will also be added.
So the final content is:
Lakehouse1, Lakehouse2, Notebook1, Notebook2, Pipeline1, SemanticModel1
Reference:
Deployment pipelines pairing behavior
NEW QUESTION # 55
You have a Fabric tenant tha1 contains a takehouse named Lakehouse1. Lakehouse1 contains a Delta table named Customer.
When you query Customer, you discover that the query is slow to execute. You suspect that maintenance was NOT performed on the table.
You need to identify whether maintenance tasks were performed on Customer.
Solution: You run the following Spark SQL statement:
REFRESH TABLE customer
Does this meet the goal?
- A. Yes
- B. No
Answer: B
Explanation:
No, the REFRESH TABLE statement does not provide information on whether maintenance tasks were performed. It only updates the metadata of a table to reflect any changes on the data files. Reference = The use and effects of the REFRESH TABLE command are explained in the Spark SQL documentation.
NEW QUESTION # 56
Drag and Drop Question
You are creating a data flow 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:
Explanation:
https://learn.microsoft.com/en-us/power-query/native-query-folding
NEW QUESTION # 57
You have a Fabric workspace named Workspace1 and an Azure SQL database.
You plan to create a dataflow that will read data from the database, and then transform the data by performing an inner join.
You need to ignore spaces in the values when performing the inner join. The solution must minimize development effort.
What should you do?
- A. Merge the queries by using a lookup table.
- B. Merge the queries by using fuzzy matching.
- C. Append the queries by using fuzzy matching.
- D. Append the queries by using a lookup table.
Answer: B
Explanation:
Fuzzy Matching: Fuzzy matching allows you to match data even when there are minor differences, such as extra spaces, in the values. This eliminates the need to manually clean or preprocess the data before the join.
https://learn.microsoft.com/en-us/powerquery-m/table-fuzzyjoin
https://learn.microsoft.com/en-us/power-query/merge-queries-fuzzy-match
NEW QUESTION # 58
You have a Microsoft Fabric tenant that contains a dataflow.
You are exploring a new semantic model.
From Power Query, you need to view column information as shown in the following exhibit.
Which three Data view options should you select? Each correct answer presents part of the solution. NOTE:
Each correct answer is worth one point.
- A. Show column value distribution
- B. Enable details pane
- C. Enable column profile
- D. Show column profile in details pane
- E. Show column quality details
Answer: B,C,E
Explanation:
To view column information like the one shown in the exhibit in Power Query, you need to select the options that enable profiling and display quality and distribution details. These are: A. Enable column profile - This option turns on profiling for each column, showing statistics such as distinct and unique values. B. Show column quality details - It displays the column quality bar on top of each column showing the percentage of valid, error, and empty values. E. Show column value distribution - It enables the histogram display of value distribution for each column, which visualizes how often each value occurs.
References: These features and their descriptions are typically found in the Power Query documentation, under the section for data profiling and quality features.
NEW QUESTION # 59
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 # 60
Drag and Drop Question
You are implementing two dimension tables named Customers and Products in a Fabric warehouse.
You need to create two slowly changing dimensions that 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 or scroll to view content.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Box 1: Type 2
Create a new version of the customer record while keeping the previous versions.
Slowly changing dimension type 2 is a method used in data warehousing to manage and track historical changes in dimension data. When an attribute value changes, a new record is created with a unique identifier, and the old record is retained. It allows for a complete historical record of changes over time, enabling accurate reporting and analysis based on different points in time.
Box 2: Type 1
Overwrite the existing value in the current record
Using slowly changing dimension type 1, if a record in a dimension table changes, the existing record is updated or overwritten. Otherwise, the new record is inserted into the dimension table.
This means records in the dimension table always reflect the current state, and no historical data is maintained. This design approach is common for columns that store supplementary values, like the email address or phone number of a customer. When a customer's email address or phone number changes, the dimension table updates the customer row with the new values. It's as if the customer always had this contact information.
Reference:
https://learn.microsoft.com/en-us/fabric/data-factory/slowly-changing-dimension-type-two
https://learn.microsoft.com/en-us/fabric/data-factory/slowly-changing-dimension-type-one
https://en.wikipedia.org/wiki/Slowly_changing_dimension
NEW QUESTION # 61
You need to recommend which type of fabric capacity SKU meets the data analytics requirements for the Research division. What should you recommend?
- A. EM
- B. F
- C. P
- D. A
Answer: B
Explanation:
You need to recommend which type of Fabric capacity SKU meets the data analytics requirements for the Research division.
Requirement: "The Research division workspaces must use a dedicated, on-demand capacity that has per- minute billing." Fabric capacity SKUs:
F (Fabric) = dedicated Fabric capacity, available on pay-as-you-go with per-minute billing.
P (Premium) and EM are Power BI capacities (not Fabric-native).
A refers to Azure Analysis Services capacity.
The only correct option is F SKU.
NEW QUESTION # 62
You have a Fabric tenant that contains a Microsoft Power BI report named Report1. Report1 includes a Python visual.
Data displayed by the visual is grouped automatically and duplicate rows are NOT displayed.
You need all rows to appear in the visual.
What should you do?
- A. Add a unique field to each row.
- B. Modify the Sort Column By property for all columns.
- C. Modify the Summarize By property for all columns.
- D. Reference the columns in the Python code by index.
Answer: C
Explanation:
By setting the "Summarize By" property to "None" for all columns, you disable automatic aggregation and ensure all rows, including duplicates, are displayed in the Python visual.
NEW QUESTION # 63
You have a Fabric tenant that contains two workspaces named Woritspace1 and Workspace2. Workspace1 contains a lakehouse named Lakehouse1. Workspace2 contains a lakehouse named Lakehouse2. Lakehouse!
contains a table named dbo.Sales. Lakehouse2 contains a table named dbo.Customers.
You need to ensure that you can write queries that reference both dbo.Sales and dbo.Customers in the same SQL query without making additional copies of the tables.
What should you use?
- A. a managed table
- B. a view
- C. a dataflow
- D. a shortcut
Answer: B
NEW QUESTION # 64
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.
Hotspot Question
You need to migrate the Research division data for Productline1. The solution must meet the data preparation requirements.
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:
Requirements: Use managed tables.
If you use saveAsTable() you don't need to specify the path "Table/"
If you you save() you specify the full path
NEW QUESTION # 65
You have a Microsoft Power BI semantic model that contains a measure named TotalSalesAmount. TotalSalesAmount returns a sales revenue amount that is translated into a selected currency.
You need to ensure that the value returned by TotalSalesAmount is formatted to use the correct currency symbol.
What should you include in the solution?
- A. the WINDOW DAX function
- B. a field parameter
- C. a dynamic format string
- D. a linguistic schema
Answer: C
NEW QUESTION # 66
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