AIF-C01 Applications of Foundation Models Practice Question
A software company is deploying a generative AI assistant on Amazon Bedrock. They need the assistant to include citations to source documents in its answers and to avoid answering when no supporting document is found. Which configuration should they use?
⚠ Common exam trap
The trap here is assuming that logging or sampling parameters can produce citations, when attribution and abstention depend on retrieval configuration and prompt constraints.
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
✓
Configure an Amazon Bedrock knowledge base and enable citation generation, and instruct the model to answer only from retrieved context.
An Amazon Bedrock knowledge base with citation generation returns responses that reference the retrieved source chunks, and a prompt that restricts answers to retrieved context makes the assistant abstain when no supporting document exists. This combination meets both the attribution and the no-support behavior requirements.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Enable model invocation logging and parse the logs to extract source references.
Why it's wrong here
Invocation logs record requests and responses for auditing; they do not cause the model to cite sources or refuse unsupported questions. Parsing logs after the fact cannot prevent an unsupported answer from reaching the user. The scenario requires citations in the response and abstention when no document is found, which must be handled during generation via retrieval and prompting.
- ✗
Use a higher temperature setting so the model explores more possible answers, including citations.
Why it's wrong here
Temperature affects randomness in token selection and does not generate citations or enforce abstention. Higher temperature increases creative variation and the risk of unsupported statements, which conflicts with the goal of grounded, cited answers. Citation behavior comes from retrieval configuration and prompt instructions, not sampling parameters.
- ✓
Configure an Amazon Bedrock knowledge base and enable citation generation, and instruct the model to answer only from retrieved context.
Why this is correct
Amazon Bedrock knowledge bases can return citations that link generated statements to the retrieved source chunks, and a system prompt can instruct the model to answer only when supporting context exists. This satisfies both requirements: source citations and abstention when no document is found. It leverages built-in retrieval and citation features rather than custom post-processing.
- ✗
Increase the model's maximum token count so it can include citations in its response.
Why it's wrong here
Maximum token count controls response length, not whether citations are produced or whether the model abstains without support. A longer response could still lack citations and could hallucinate when no documents are retrieved. The requirement is about grounding and attribution, which depends on retrieval and citation configuration, not output length.
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JA
Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Amazon Web Services exam blueprint
This AIF-C01 practice question is part of Courseiva's free Amazon Web Services certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the AIF-C01 exam.