Module Assessment

1.

A developer is creating a container to store knowledge base documents with embeddings generated by the text-embedding-ada-002 model. Which vector policy configuration correctly specifies the embedding path for this model?

Set dataType to float32, dimensions to 1536, and distanceFunction to cosine

Set dataType to int8, dimensions to 1024, and distanceFunction to euclidean

Set dataType to float16, dimensions to 3072, and distanceFunction to dotproduct

2.

An AI application executes vector searches that return 100 results to display to users, but query performance is slow and RU consumption is high. Which change would most effectively improve performance while maintaining search quality?

Force brute-force search by setting the third parameter of VectorDistance to true

Remove the ORDER BY clause from the query

Reduce the TOP N clause to return only 10-20 results

3.

A support knowledge base application needs to find documents similar to a user's query but only within a specific product category. Which query structure efficiently combines vector search with metadata filtering?

Execute a cross-partition vector search first, then filter results by category in application code

Include the category filter in the WHERE clause and specify the partition_key parameter when the category is the partition key

Create a separate container for each product category and execute vector searches against the appropriate container

4.

A development team wants to implement search that combines semantic understanding with exact keyword matching for technical terms and error codes. Which approach enables this hybrid search capability?

Use ORDER BY RANK RRF with VectorDistance and FullTextScore functions

Execute two separate queries and merge results in application code

Add keyword terms to the query vector before executing vector search

5.

An AI application needs to keep vector embeddings synchronized with document content as documents are updated. Which approach provides reliable, automatic embedding refresh without polling?

Schedule a batch job to periodically scan all documents and regenerate embeddings

Store a timestamp with each document and query for recently modified documents

Use an Azure Functions Cosmos DB trigger to detect changes and regenerate embeddings

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