Learn Spring AI – Module 3 Lesson 4 – Quiz

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Question 1 of 4

1. Question

How does Spring AI’s Advisor pattern address cross-cutting concerns such as grounding prompts or logging requests?

It requires us to re-implement the same plumbing in every controller that talks to the model
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It retrains the model so the cross-cutting behavior becomes part of its weights
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It provides a chain of interceptors that wraps the model call, letting each one inspect or modify the request on the way in and the response on the way out
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It replaces ChatClient with a lower-level API that exposes the raw HTTP request
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Question 2 of 4

2. Question

What does the QuestionAnswerAdvisor do for us out of the box? (select all that apply)

Trains the embedding model on the chunks it retrieves
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Runs a similarity search against the configured vector store
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Rewrites the prompt using a template that fills {query} and {question_answer_context} with the retrieved chunks
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Bundles the retrieve-augment-generate pipeline into a single component
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Question 3 of 4

3. Question

What is the trade-off of raising the topK value in the SearchRequest we pass to the QuestionAnswerAdvisor?

A larger topK broadens recall, but the augmented prompt grows and can push past the model's context window
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A larger topK always improves the grounded answer with no downside
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A larger topK reduces the number of chunks the advisor pulls from the store
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A larger topK turns off the similarityThreshold filter
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Question 4 of 4

4. Question

What does adding .filterExpression(“file_name == ‘policies.pdf’”) to the SearchRequest achieve?

It re-embeds only the policies.pdf file so its vectors are more accurate
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It restricts retrieval to chunks whose file_name metadata matches policies.pdf, applied before the topK cut
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It renames the matching file to policies.pdf in the vector store
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It lowers the similarityThreshold for chunks coming from that file
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Learn Spring AI – Module 3 Lesson 4 – Quiz

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