You have provisioned Azure Cognitive Search resource and are using it for your full text based searches for users to search answers to their questions. Your search engine maintains the relevant scores for the documents based on the search items. You observe and conclude that during wildcard searches and in searches where scores are different for the same phrase, the relevant scores need to be tuned. Which ways would you use in Azure Cognitive Search to tune the relevant scores? (select two answer choices)

Answer options:

A.Term boosting
B.Lexical analysis
C."searchMode": "any"
D.Scoring profiles

Correct Answers: A and D
Option A is correct because term boosting can boost the score of a document that contains the boosted term in comparison to the one that does not contain it. While a scoring profile is applied to boost the score based on the field, It can further boost it by ranking certain terms higher than others. It does that by using a symbol and a number as suffix to the search term to distinguish it from other search terms and rank higher in the relevant score.
Option B is incorrect because the lexical analysis uses the lexical analyzers to process term queries and phrase queries. It can remove the non-essential words but cannot tune the relevant scores.
Option C is incorrect because the searchmode parameter is used to match any or all terms. This parameter is not helpful in this scenario.
Option D is correct because the scoring profiles are used to boost the scores of the matches. You can boost scores based on the newer items or revenue potential using scoring profiles. They are part of the index. Use parameter scoringProfile in your search index to call a scoring profile.
Reference:
To learn more about tuning scores in full text search in Azure, use the link given below: 

https://docs.microsoft.com/en-us/azure/search/search-lucene-query-architecture