AIF-C01 Fundamentals of Generative AI Practice Question
A product team wants its generative AI assistant to answer questions about internal policy documents accurately rather than from the model's general training knowledge. Which TWO techniques should they apply? (Choose two.)
⚠ Common exam trap
The trap here is treating generation settings such as temperature or prompt length as accuracy controls, when grounding depends on supplying evidence and constraining the model to it.
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
✓
Instruct the model to answer only from the provided context and to state when the context is insufficient.
Grounding combines retrieval of authoritative source passages with an instruction that limits the model to that evidence and requires an explicit fallback when evidence is missing. Together these reduce reliance on parametric knowledge and make answers traceable to internal documents, which is what accurate policy question answering demands.
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 temperature setting so the model explores a wider range of possible answers.
Why it's wrong here
Higher temperature increases randomness and diversity of token selection, which makes outputs less deterministic and more prone to drifting away from the source material. For factual policy question answering, this works against accuracy, so raising temperature is the opposite of what a grounding-focused team should do.
- ✗
Shorten the system prompt to a single sentence to reduce token consumption.
Why it's wrong here
Trimming the system prompt saves tokens but removes the behavioral guardrails that keep responses on topic and grounded. Brevity is not a grounding mechanism, and a terse prompt can make the model more likely to answer from general knowledge, which is precisely what the team wants to avoid.
- ✓
Instruct the model to answer only from the provided context and to state when the context is insufficient.
Why this is correct
An explicit instruction that constrains the model to the supplied context, plus a fallback behavior for missing information, prevents the model from filling gaps with plausible but invented policy details. This complements retrieval by defining how the model should behave when retrieved evidence is absent or incomplete.
- ✗
Ask the model to produce the longest possible answer for every question.
Why it's wrong here
Longer answers do not improve factual accuracy and often introduce additional unsupported statements as the model continues generating. Verbosity increases cost and review burden without addressing whether claims are traceable to policy documents, so it is not a valid grounding technique for this use case.
- ✓
Retrieve relevant passages from the policy corpus and include them in the prompt before generation.
Why this is correct
Supplying retrieved, authoritative passages gives the model the actual policy text to condition on, which anchors the answer in the organization's documents instead of relying on memorized training data. This is the core grounding technique for question answering over private corpora and directly reduces unsupported claims about internal policy.
Go deeper
Related to this question
About these practice questions
This AIF-C01 question is part of Courseiva's 862-question bank — original exam-style content with full explanations and wrong-answer analysis, never real exam questions or exam dumps. Learn why practice questions differ from exam dumps →
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.