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Answer: Analyze the execution plan of each visual to identify and optimize any inefficient queries, ensuring that the underlying data retrieval is as efficient as possible.
Option A is the most effective approach because it directly addresses the root cause of the performance issues by optimizing the queries that power each visual. This method is cost-effective, as it does not require additional hardware resources, and it maintains the report's intuitiveness and clarity. Option B, while reducing the number of queries, compromises the report's usability. Option C provides a temporary fix without solving the underlying inefficiencies, and Option D, though beneficial for performance, does not directly optimize the queries that are causing the slow load times.
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As a Microsoft Fabric Analytics Engineer Associate, you are working on optimizing the performance of an enterprise-scale semantic model. The model supports a Power BI report that includes multiple visuals, each based on different tables within the model. Users have reported that the report is experiencing slow load and render times. Considering the need for a solution that is cost-effective, scalable, and maintains the report's intuitiveness, which of the following actions would you prioritize to improve the report's performance? (Choose one option)
A
Analyze the execution plan of each visual to identify and optimize any inefficient queries, ensuring that the underlying data retrieval is as efficient as possible.
B
Merge all visuals into a single visual to minimize the number of queries executed against the semantic model, despite potential impacts on user experience and report clarity.
C
Upgrade the hardware resources of the Power BI service to enhance processing power and memory, without addressing the root cause of the performance issues.
D
Implement a caching strategy to store pre-computed results for frequently accessed data, reducing the need for real-time calculations and data retrieval.
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