- (Topic 2)
You have a Fabric tenant that contains a lakehouse named Lakehouse1.
You need to prevent new tables added to Lakehouse1 from being added automatically to the default semantic model of the lakehouse.
What should you configure? (5)

Correct Answer:A
To prevent new tables added to Lakehouse1 from being automatically added to the default semantic model, you should configure the semantic model settings. There should be an option within the settings of the semantic model to include or exclude new tables by default. By adjusting these settings, you can control the automatic inclusion of new tables.
References: The management of semantic models and their settings would be covered under the documentation for the semantic layer or modeling features of the Fabric tenant's lakehouse solution.

HOTSPOT - (Topic 1)
You need to design a semantic model for the customer satisfaction report.
Which data source authentication method and mode should you use? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Solution:
For the semantic model design required for the customer satisfaction report, the choices for data source authentication method and mode should be made based on security and performance considerations as per the case study provided.
Authentication method: The data should be accessed securely, and given that row-level security (RLS) is required for users executing T-SQL queries, you should use an authentication method that supports RLS. Service principal authentication is suitable for automated and secure access to the data, especially when the access needs to be controlled programmatically and is not tied to a specific user's credentials.
Mode: The report needs to show data as soon as it is updated in the data store, and it should only contain data from the current and previous year. DirectQuery mode allows for real-time reporting without importing data into the model, thus meeting the need for up-to- date data. It also allows for RLS to be implemented and enforced at the data source level, providing the necessary security measures.
Based on these considerations, the selections should be:
✑ Authentication method: Service principal authentication
✑ Mode: DirectQuery

Does this meet the goal?

Correct Answer:A

- (Topic 2)
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)

Correct Answer:A
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. References = You can refer to the official Apache Spark documentation for more details on joins and the broadcast hint.

- (Topic 1)
Which type of data store should you recommend in the AnalyticsPOC workspace?

Correct Answer:C
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. References = For further understanding, Microsoft's documentation on the lakehouse architecture provides insights into how it supports various data types and analytical operations.

- (Topic 1)
What should you recommend using to ingest the customer data into the data store in the AnatyticsPOC workspace?

Correct Answer:D
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. References = You can learn more about dataflows and their use in Power BI environments in Microsoft's Power BI documentation.

- (Topic 2)
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?

Correct Answer:C
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.