While processing a search request using full text search in Azure, which key component would you use for matching document scores based on inverted indexes.
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A. B. C. D.Correct Answer: B.
Option A is incorrect because the query parsers are used to send query structure to the search engine in the initial phase of query processing.
They separate query terms from query operators before creating the query structure.
Option B is correct because the search engine is used to retrieve and rank the scores based on relevance.
Option C is incorrect because the index (inverted) is the storage structure that stores and maintains a sorted list of the terms extracted from search documents.
It also maps each search term to the document list where it is maintained.
Option D is incorrect because the analyzers are used to process strings during query execution such as removal of non-essential words or converting text from uppercase to lowercase.
Reference:
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When processing a search request using full-text search in Azure, the key component for matching document scores based on inverted indexes is the Index.
Inverted indexes are used to enable fast text searches by creating a data structure that lists every unique word or term and the documents in which they appear. The index can then be used to quickly find all documents containing a specific term or set of terms.
When a user submits a search query, the query is first analyzed and tokenized by an Analyzer, which breaks the query down into individual terms and applies any necessary transformations (such as stemming or case normalization).
The Query Parser then takes these terms and constructs a search query that can be executed against the index. The search engine then uses the index to retrieve a set of matching documents, and calculates a score for each document based on how well it matches the query.
The score is calculated by comparing the frequency of each query term in the document against the overall frequency of that term in the entire index. This is known as the term frequency-inverse document frequency (TF-IDF) algorithm.
Finally, the search engine sorts the matching documents by score and returns them to the user as search results.
In summary, while both the Query Parser and Analyzer are important components of a full-text search system, the key component for matching document scores based on inverted indexes is the Index.