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Answer: Analyze the translations to identify inefficiencies and refactor them using more efficient translation management and storage strategies, such as leveraging language-specific indexes or partitioning.
Option B is the correct approach because it directly addresses the root cause of the performance issues by optimizing the translation management and storage strategies, which can significantly improve the model's performance while maintaining multilingual support. Option A is not feasible as it removes essential functionality. Option C may reduce processing time but at the cost of manual effort and potential delays in updates. Option D is a temporary fix that does not solve the inefficiency in translation management and could lead to higher costs without substantial performance gains.
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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 translations, which is currently experiencing performance issues due to the complexity of managing these translations. The model is critical for a global application that supports multiple languages. Given the constraints of maintaining multilingual support without compromising 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 translations and use a single language for the model to simplify processing.
B
Analyze the translations to identify inefficiencies and refactor them using more efficient translation management and storage strategies, such as leveraging language-specific indexes or partitioning.
C
Disable the automatic processing of translations and switch to manual processing only when updates are necessary, to reduce runtime overhead.
D
Increase the memory allocation for the semantic model processing engine to accommodate the complex translations without addressing the underlying inefficiencies.
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