- A
Switch to a larger model with more parameters
Why wrong: Larger models may have stronger pre-training knowledge and could be even more prone to ignoring context.
- B
Reduce the temperature to make the model more deterministic and adhere to the context
Lower temperature reduces randomness, making the model more likely to follow instructions and stick to the provided context.
- C
Increase the number of retrieved documents in the context window
Why wrong: More documents may confuse the model further; the issue is the model ignoring context, not lack of context.
- D
Decrease the top-p value to 0.1 to restrict token selection
Why wrong: Top-p controls cumulative probability; while it can reduce diversity, lowering temperature is the more direct fix for adherence to context.
AIF-C01 Generative AI and Foundation Models Practice Question
This AIF-C01 practice question tests your understanding of generative ai and foundation models. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. 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.
A company is using a RAG system with Amazon Titan Text Express for question answering. They notice that the model frequently ignores the retrieved context and generates answers based on its pre-training knowledge, leading to incorrect responses. Which change would MOST directly address this issue?
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
Reduce the temperature to make the model more deterministic and adhere to the context
Reducing the temperature parameter makes the model more deterministic, which increases the likelihood that it will follow the provided context rather than relying on its pre-training knowledge. In a RAG system, lower temperature forces the model to assign higher probabilities to tokens that align with the retrieved context, directly addressing the issue of ignoring context.
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.
- ✗
Switch to a larger model with more parameters
Why it's wrong here
Larger models may have stronger pre-training knowledge and could be even more prone to ignoring context.
- ✓
Reduce the temperature to make the model more deterministic and adhere to the context
Why this is correct
Lower temperature reduces randomness, making the model more likely to follow instructions and stick to the provided context.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Increase the number of retrieved documents in the context window
Why it's wrong here
More documents may confuse the model further; the issue is the model ignoring context, not lack of context.
- ✗
Decrease the top-p value to 0.1 to restrict token selection
Why it's wrong here
Top-p controls cumulative probability; while it can reduce diversity, lowering temperature is the more direct fix for adherence to context.
Common exam traps
Common exam trap: answer the scenario, not the keyword
A common mistake on this exam is thinking that increasing the number of retrieved documents or switching to a larger Amazon Titan model will improve context adherence, when in fact the key lever is the temperature parameter, which controls how strictly the model follows the provided context.
Trap categories for this question
Similar concept trap
More documents may confuse the model further; the issue is the model ignoring context, not lack of context.
Detailed technical explanation
How to think about this question
Temperature controls the softmax scaling of logits; lower values (e.g., 0.1) sharpen the probability distribution, making the model more likely to choose the highest-probability token, which in a RAG setup often corresponds to tokens grounded in the retrieved context. In contrast, top-p (nucleus sampling) dynamically selects a set of tokens whose cumulative probability exceeds the threshold, which can still allow the model to drift from context if the context tokens are not in that set. Real-world tuning often involves setting temperature between 0.1 and 0.3 for RAG tasks to balance faithfulness and fluency.
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
Got this wrong? Here's your next step.
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FAQ
Questions learners often ask
What does this AIF-C01 question test?
Generative AI and Foundation Models — This question tests Generative AI and Foundation Models — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Reduce the temperature to make the model more deterministic and adhere to the context — Reducing the temperature parameter makes the model more deterministic, which increases the likelihood that it will follow the provided context rather than relying on its pre-training knowledge. In a RAG system, lower temperature forces the model to assign higher probabilities to tokens that align with the retrieved context, directly addressing the issue of ignoring context.
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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