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)
Click on the arrows to vote for the correct answer
A. B. C. D.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:
Azure Cognitive Search provides several ways to tune the relevance scores of documents during search. Two of the methods that can be used to achieve this are Term Boosting and Scoring Profiles.
Term Boosting: Term boosting is a technique that assigns a higher weight to specific terms in a search query, increasing their relevance in search results. This technique can be used to increase the relevance of specific terms in search queries that are critical to business or user needs. For example, in a document search scenario, term boosting can be used to give more importance to certain keywords like the document title or author name. Term boosting can be achieved in Azure Cognitive Search by specifying a weight for specific terms using the 'boost' parameter. The higher the boost value, the more weight the term will have in the relevance score.
Scoring Profiles: Scoring profiles allow you to customize how relevance scores are calculated for search results. They are used to control the factors that affect the relevance score of search results such as term frequency, proximity, and other factors. With Scoring Profiles, you can adjust the weight of these factors based on the business needs or user requirements. Scoring profiles can also be used to adjust relevance scores for specific scenarios such as wildcard searches or searches where scores are different for the same phrase. In Azure Cognitive Search, Scoring profiles can be defined in the Azure portal or by using an API. Once a Scoring Profile is created, it can be associated with an index, and Azure Cognitive Search will automatically apply the profile to calculate relevance scores for search results.
In addition to the above, lexical analysis is a technique used to parse text and extract tokens, which are individual words or phrases, from the text. It is used to enable full-text search in Azure Cognitive Search and is not directly related to tuning relevance scores.
The "searchMode": "any" parameter is used to configure the search mode to retrieve documents that contain any of the specified search terms. It is not directly related to tuning relevance scores.