AIF-C01 Fundamentals of Generative AI Practice Question
A company uses Amazon Bedrock Agents to build an agent that interacts with users through a chat interface. The agent is configured with a knowledge base containing product documentation. Sometimes the agent fails to answer simple questions like 'What is your return policy?' and instead says it cannot find the answer. The knowledge base does contain the return policy. What is the most likely reason?
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Simplify and clarify the agent's instruction prompt to emphasize knowledge base usage
The agent's instruction prompt might be too complex or not explicitly directing the agent to use the knowledge base. Simplifying the prompt to clearly instruct the agent to first search the knowledge base can resolve the issue. Increasing timeout or adding more data is unnecessary. A stronger model may help but is not the root cause.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Increase the agent's maximum timeout for processing
Why it's wrong here
Timeout settings govern how long the agent waits for a response, not whether retrieval returns relevant passages; exceeding a timeout produces a timeout error, not a 'cannot find the answer' message. Raising timeouts is tempting when responses feel slow, but the symptom here is retrieval failure, pointing to knowledge base configuration or chunking instead.
- ✗
Use a more powerful foundation model for reasoning
Why it's wrong here
A stronger foundation model improves reasoning over retrieved context, but it cannot answer from passages the retrieval step never returned; the failure is in knowledge base retrieval, not generation. Upgrading the model is tempting because weak models do give poor answers, yet the agent explicitly reports the answer is missing, indicating an ingestion or chunking issue.
- ✗
Add more documents to the knowledge base
Why it's wrong here
The return policy already exists in the knowledge base, so adding documents does not address why retrieval misses it; ingestion, chunking, or embedding configuration is the likely fault. Expanding the corpus is tempting because sparse knowledge bases do cause poor answers, but here the content is present and simply not being surfaced.
- ✓
Simplify and clarify the agent's instruction prompt to emphasize knowledge base usage
Why this is correct
Overly complex or ambiguous instructions can cause the agent to under-utilise its knowledge base, producing false 'cannot find' responses despite the return policy existing. Clarifying the prompt to explicitly prioritise knowledge base retrieval satisfies the stem's scenario, restoring correct answers.
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