At ValidExamDumps, we consistently monitor updates to the Microsoft DP-600 exam questions by Microsoft. Whenever our team identifies changes in the exam questions,exam objectives, exam focus areas or in exam requirements, We immediately update our exam questions for both PDF and online practice exams. This commitment ensures our customers always have access to the most current and accurate questions. By preparing with these actual questions, our customers can successfully pass the Microsoft Implementing Analytics Solutions Using Microsoft Fabric exam on their first attempt without needing additional materials or study guides.
Other certification materials providers often include outdated or removed questions by Microsoft in their Microsoft DP-600 exam. These outdated questions lead to customers failing their Microsoft Implementing Analytics Solutions Using Microsoft Fabric exam. In contrast, we ensure our questions bank includes only precise and up-to-date questions, guaranteeing their presence in your actual exam. Our main priority is your success in the Microsoft DP-600 exam, not profiting from selling obsolete exam questions in PDF or Online Practice Test.
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?
In a Fabric Data Factory pipeline, to execute a stored procedure and make the returned values available for downstream activities, the Lookup activity is used. This activity can retrieve a dataset from a data store and pass it on for further processing. Here's how you would use the Lookup activity in this context:
Add a Lookup activity to your pipeline.
Configure the Lookup activity to use the stored procedure by providing the necessary SQL statement or stored procedure name.
In the settings, specify that the activity should use the stored procedure mode.
Once the stored procedure executes, the Lookup activity will capture the results and make them available in the pipeline's memory.
Downstream activities can then reference the output of the Lookup activity.
You have a Microsoft Power B1 Premium Per User (PPU) workspace that contains a semantic model.
You have an Azure App Service app named App1 that modifies row-level security (RLS) for the model by using the XMLA endpoint. App1 requires users to sign in by using their Microsoft Entra credentials to access the XMLA endpoint. You need to configure App1 to use a service account to access the model. What should you do first?
Which type of data store should you recommend in the AnalyticsPOC workspace?
A lakehouse (C) should be recommended for the AnalyticsPOC workspace. It combines the capabilities of a data warehouse with the flexibility of a data lake. A lakehouse supports semi-structured and unstructured data and allows for T-SQL and Python read access, fulfilling the technical requirements outlined for Litware. Reference = For further understanding, Microsoft's documentation on the lakehouse architecture provides insights into how it supports various data types and analytical operations.
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?
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.
You have a Fabric tenant that contains a lakehouse. You plan to use a visual query to merge two tables.
You need to ensure that the query returns all the rows that are present in both tables. Which type of join should you use?
When you need to return all rows that are present in both tables, you use a full outer join. This type of join combines the results of both left and right outer joins and returns all rows from both tables, with matching rows from both sides where available. If there is no match, the result is NULL on the side of the join where there is no match.
