- A
Include relevant context from the knowledge base in the prompt
RAG relies on accurate context to ground responses.
- B
Increase the max_tokens parameter
Why wrong: Longer output does not improve accuracy; may introduce hallucinations.
- C
Provide clear instructions in the system prompt
System prompts guide the model to use the provided context.
- D
Use the largest foundation model available
Why wrong: Larger model does not guarantee factual accuracy; may increase latency and cost.
- E
Increase the temperature parameter
Why wrong: Higher temperature increases creativity, reducing factual accuracy.
Question 611 of 1,000
AIF-C01 Applications of Foundation Models Practice Question
This AIF-C01 practice question tests your understanding of applications of foundation models. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
Which TWO actions are recommended for improving the factual accuracy of a foundation model's responses when using RAG?
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
✓
Include relevant context from the knowledge base in the prompt
A is correct because RAG (Retrieval-Augmented Generation) improves factual accuracy by injecting retrieved, relevant context from an external knowledge base directly into the prompt. This grounds the model's response in verifiable data, reducing hallucinations and ensuring the output is based on the provided sources rather than the model's internal parametric knowledge alone.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Include relevant context from the knowledge base in the prompt
Why this is correct
RAG relies on accurate context to ground responses.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Increase the max_tokens parameter
Why it's wrong here
Longer output does not improve accuracy; may introduce hallucinations.
- ✓
Provide clear instructions in the system prompt
Why this is correct
System prompts guide the model to use the provided context.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Use the largest foundation model available
Why it's wrong here
Larger model does not guarantee factual accuracy; may increase latency and cost.
- ✗
Increase the temperature parameter
Why it's wrong here
Higher temperature increases creativity, reducing factual accuracy.
Common exam traps
Common exam trap: answer the scenario, not the keyword
AWS often tests the misconception that larger models or higher randomness (temperature) inherently improve response quality, but in RAG, factual accuracy depends on retrieval quality and prompt engineering, not model size or creativity parameters.
Trap categories for this question
Command / output trap
Longer output does not improve accuracy; may introduce hallucinations.
Detailed technical explanation
How to think about this question
In RAG, the retrieval step typically uses vector embeddings and similarity search (e.g., cosine similarity) to fetch the most relevant document chunks from a vector database like FAISS or Pinecone. These chunks are then concatenated into the prompt, often with a separator token, so the model attends to them during generation. A subtle behavior is that the model may still ignore the retrieved context if the prompt structure is weak, which is why clear instructions in the system prompt (option C) are also critical to guide the model to prioritize the provided context over its own training data.
KKey Concepts to Remember
- Read the scenario before looking for a memorised answer.
- Find the constraint that changes the correct option.
- Eliminate answers that are true in general but not in this case.
TExam Day Tips
- Watch for words such as best, first, most likely and least administrative effort.
- Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
What to study next
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FAQ
Questions learners often ask
What does this AIF-C01 question test?
Applications of Foundation Models — This question tests Applications of Foundation Models — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Include relevant context from the knowledge base in the prompt — A is correct because RAG (Retrieval-Augmented Generation) improves factual accuracy by injecting retrieved, relevant context from an external knowledge base directly into the prompt. This grounds the model's response in verifiable data, reducing hallucinations and ensuring the output is based on the provided sources rather than the model's internal parametric knowledge alone.
What should I do if I get this AIF-C01 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Last reviewed: Jul 4, 2026
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