The answer is a temperature setting of zero. When temperature is set to zero, the model becomes entirely deterministic, always selecting the single highest-probability token at each step. This eliminates all randomness, so if the most likely next token is the same word just generated, the model enters a repetitive loop, outputting that word over and over. On the AWS Certified AI Practitioner AIF-C01 exam, this scenario tests your understanding of how the temperature parameter controls creativity versus determinism in generative AI models like those accessed via Amazon Bedrock. A common trap is confusing temperature with top-p or top-k sampling, but only temperature zero forces such rigid repetition. Remember the memory tip: zero temperature equals zero creativity, meaning the model will freeze into a single-word rut.
AIF-C01 Fundamentals of Generative AI Practice Question
This AIF-C01 practice question tests your understanding of fundamentals of generative ai. 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.
Exhibit
Refer to the exhibit.
```
{
"modelId": "amazon.titan-text-lite-v1",
"contentType": "application/json",
"accept": "application/json",
"body": {
"inputText": "Summarize the following meeting notes: ...",
"textGenerationConfig": {
"maxTokenCount": 100,
"stopSequences": [],
"temperature": 0,
"topP": 0.9
}
}
}
```
A developer is using the Amazon Bedrock InvokeModel API with the above request to summarize meeting notes. The response is a single word repeated many times. Which parameter is MOST likely causing this issue?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue: "most likely"
Why it matters: Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
temperature set to 0
A temperature of 0 forces the model to always select the highest-probability token at each step, which can lead to repetitive loops if the most likely token repeatedly points back to itself (e.g., the same word). This deterministic behavior eliminates randomness, causing the model to get stuck in a single-word cycle rather than generating diverse or coherent text.
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.
✗
topP set to 0.9
Why it's wrong here
This is a moderate value that should not cause repetition.
✗
stopSequences is empty
Why it's wrong here
Empty stop sequences are normal.
✗
maxTokenCount set to 100
Why it's wrong here
Max token count limits length, not repetition.
✓
temperature set to 0
Why this is correct
Temperature 0 makes output deterministic and prone to repetition.
Clue confirmation
The clue word "most likely" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
AWS often tests the misconception that temperature only affects 'creativity' or 'randomness,' when in fact a temperature of 0 causes deterministic argmax selection, which can paradoxically produce repetitive or stuck outputs rather than simply 'less creative' text.
Detailed technical explanation
How to think about this question
Temperature controls the softmax scaling of logits before sampling: at temperature=0, the softmax becomes a one-hot argmax, always picking the token with the highest logit. If the model assigns the highest logit to the same word repeatedly (e.g., due to a biased training distribution or short context), it enters a degenerate loop. In practice, this parameter is often tuned between 0.1 and 1.0 for summarization tasks to balance coherence and creativity; values near 0 are reserved for tasks requiring strict factual recall, not open-ended generation.
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.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Fundamentals of Generative AI — This question tests Fundamentals of Generative AI — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: temperature set to 0 — A temperature of 0 forces the model to always select the highest-probability token at each step, which can lead to repetitive loops if the most likely token repeatedly points back to itself (e.g., the same word). This deterministic behavior eliminates randomness, causing the model to get stuck in a single-word cycle rather than generating diverse or coherent text.
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.
Are there clue words in this question I should notice?
Yes — watch for: "most likely". Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Question Discussion
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