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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Prepare data for analytics | 45โ50 | - Implement data storage structures
|
| Topic 2: Maintain a data analytics solution | 25โ30 | - Implement security and governance
|
| Topic 3: Implement and manage semantic models | 25โ30 | - Deploy and maintain semantic models
|
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NEW QUESTION # 175
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?
Answer: D
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 # 176
You have a Fabric tenant that contains two workspaces named Workspace1 and Workspace2.
Workspace1 is used as the development environment.
Workspace2 is used as the production env ironment.
Each environment uses a different storage account.
Workspace1 contains a Dataflow Gen2 named Dataflow1. The data source of Dataflow1 is a CSV file in blob storage.
You plan to implement a deployment pipeline to deploy items from Workspace1 to Wor kspace2.
You need to ensure that the data source references the correct location in the production environment.
What should you do?
Answer: C
Explanation:
Scenario:
Dev = Workspace1 with Dataflow Gen2 (source = blob storage CSV).
Prod = Workspace2, different storage account.
Need: ensure deployed dataflow points to the production storage location .
Analysis:
Data source rules : used to remap data sources between environments (e.g., blob storage dev # blob storage prod).
Parameter rules : used when the data source location is parameterized (for example, a parameter storing the file path or connection string).
Best practice: use parameters in the dataflow for connection strings, then apply parameter rules in deployment pipelines.
In this case, since the requirement is about ensuring the reference updates correctly, only parame ter rules are needed (not data source rules).
B). Create a parameter rule only.
NEW QUESTION # 177
You have a Fabric workspace named Workspace1 that is assigned to a newly created Fabric capacity named Capacity1.
You create a semantic model named Model1 and deploy Model1 to Workspace1.
You need to publish changes to Model1 directly from Tabular Editor.
What should you do?
Answer: A
NEW QUESTION # 178
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?
Answer: D
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.
References = For more information, please visit the official documentation on T-SQL queries and the GROUP BY clause at T-SQL GROUP BY.
NEW QUESTION # 179
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?
Answer: B
Explanation:
Lookup activity can execute a SQL query or stored procedure and retrieve a single row or dataset. The returned values can then be passed to downstream pipeline activities.
Switch is for branching logic, not executing procedures.
Append variable just adds values to a variable.
KQL activity executes Kusto queries, not SQL stored procedures.
Therefore, the correct choice is Lookup.
Reference: Lookup activity in Fabric pipelines
NEW QUESTION # 180
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