What DP-800 Questions and Answers feature?

where exists 2. where p.PatientID = pr.PatientID Otherwise we filter PatientID twice. Having in the subquery does not work without group by and the date filter does not work without where.

Verified Answer : =

queryType → " semantic "

semanticConfiguration → " hotels "

captions → " extractive "

Comprehensive and Detailed Explanation with all Developing AI-Enabled Database Solutions documents : =

The correct configuration is queryType: " semantic " , semanticConfiguration: " hotels " , and captions: " extractive " .

The request is already a hybrid search because it contains both a textual search value ( " ocean view " ) and a vectorQueries block targeting DescriptionVector . Microsoft documents that hybrid search executes full-text and vector searches together and merges their result sets. When semantic ranking is required, queryType must be set to " semantic " .

The semanticConfiguration value must reference the semantic configuration defined for the target index. Since the index in this question is named hotels and the available answer choice is " hotels " , that is the appropriate configuration value in the supplied answer set. Microsoft’s REST examples show that semantic queries require both queryType: " semantic " and a valid semanticConfiguration .

To return semantic captions, use " captions " : " extractive " . Microsoft states that extractive captions identify the most relevant passages from the indexed content and return them with each result. They are generated from existing document text rather than generated by an LLM.

The completed request body is therefore:

{

" search " : " ocean view " ,

" queryType " : " semantic " ,

" semanticConfiguration " : " hotels " ,

" captions " : " extractive " ,

" top " : 10,

" hybridSearch " : {

" maxTextRecallSize " : 50

},

" vectorQueries " : [

{

" kind " : " vector " ,

" vector " : [ /* embedding array */ ],

" fields " : " DescriptionVector " ,

" k " : 50

}

]

}

Final drag-and-drop selections:

First target: " semantic "

Second target: " hotels "

Third target: " extractive "

The correct answer is B because the problem is not the vector search operator itself. The problem is that embeddings are becoming stale when article content changes . Microsoft documents that change data capture (CDC) tracks insert, update, and delete operations on source tables, which makes it the right mechanism to identify only the rows that changed.

This also best satisfies the requirement to minimize CPU usage on SalesDB . With CDC, the database only records the row changes, and the embedding regeneration work can be moved to an external process such as an Azure Functions app. That avoids running embedding generation inline inside the database for every update and avoids repeatedly recalculating embeddings for unchanged rows. In contrast, an hourly full-table regeneration would be extremely wasteful on a table with two million frequently updated articles, and a trigger that calls embedding generation per row would push expensive AI work into the transactional path of the database.

Option A is incorrect because changing from VECTOR_SEARCH to VECTOR_DISTANCE does not regenerate embeddings; it only changes the retrieval method. Microsoft states that VECTOR_SEARCH is the ANN search function, while VECTOR_DISTANCE performs exact distance calculation, so neither option addresses stale embedding data.

So the right design is:

use CDC to detect only changed articles,

process those changes outside the database,

regenerate embeddings only for changed rows,

write back the refreshed embeddings for current semantic search results.