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Answer: All of the above
Optimizing search performance and relevance in Azure AI Search for a large e-commerce website with millions of products requires a combination of techniques. Using custom analyzers allows you to define specific language rules and improve the tokenization process, which can enhance the search performance and accuracy. Implementing scoring profiles enables you to customize the ranking of search results based on specific criteria, such as product popularity, relevance, or user preferences. Using synonyms and suggesters can improve the search experience by handling variations in user queries and providing suggestions for alternative search terms. All of these techniques are important to optimize the search performance and relevance for a large e-commerce website.
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In Azure AI Search, how can you optimize the search performance and relevance of the search results for a large e-commerce website with millions of products?
A
Use custom analyzers to define specific language rules and improve the tokenization process.
B
Implement scoring profiles to customize the ranking of search results based on specific criteria.
C
Use synonyms and suggesters to improve the search experience and handle variations in user queries.
D
All of the above
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