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Answer: Analyze the calculations to identify inefficiencies and refactor them using more efficient DAX expressions and functions, ensuring the model's performance improves without losing data integrity.
Option B is the correct approach because it directly addresses the root cause of the performance issues by optimizing the calculations themselves, which is the most cost-effective and efficient method. It ensures the model remains accurate and fast without unnecessary expenses or data loss. Option A might lead to loss of valuable insights, Option C could delay critical financial reporting, and Option D would increase costs without solving the underlying inefficiency.
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As a Microsoft Fabric Analytics Engineer Associate, you are tasked with optimizing a semantic model that contains a large number of calculated columns, which is causing performance issues due to the complexity of these calculations. The model is critical for real-time analytics in a financial reporting scenario where accuracy and speed are paramount. Given the constraints of minimizing cost without compromising on performance, which of the following steps would you take to optimize the performance of the semantic model? (Choose the best option.)
A
Remove all the calculated columns and replace them with measures, despite the potential loss of detailed financial insights.
B
Analyze the calculations to identify inefficiencies and refactor them using more efficient DAX expressions and functions, ensuring the model's performance improves without losing data integrity.
C
Disable the automatic calculation of the columns and manually trigger the calculation during off-peak hours to reduce load, accepting a delay in data availability.
D
Increase the memory allocation for the semantic model processing engine, despite the higher operational costs, to handle the complex calculations without refactoring.
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