Generative AI Leader Temperature Practice Question
This Generative AI Leader practice question tests your understanding of techniques to improve generative ai model output. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. A key principle to apply: temperature. 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. The following is a configuration block from a Vertex AI PaLM API request:
{
"instances": [{"context": "You are a helpful assistant.", "messages": [{"author": "user", "content": "Explain quantum computing"}]}],
"parameters": {
"temperature": 0.9,
"maxOutputTokens": 1000,
"topK": 40,
"topP": 0.95,
"candidateCount": 1
}
}
A user reports that the model's response to the same prompt varies significantly across different calls. Which parameter change would most likely reduce variability?
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.
Exhibit
Refer to the exhibit. The following is a configuration block from a Vertex AI PaLM API request:
{
"instances": [{"context": "You are a helpful assistant.", "messages": [{"author": "user", "content": "Explain quantum computing"}]}],
"parameters": {
"temperature": 0.9,
"maxOutputTokens": 1000,
"topK": 40,
"topP": 0.95,
"candidateCount": 1
}
}
A
Decrease topK to 10.
Why wrong: Lower topK reduces diversity but temperature is more impactful.
B
Decrease temperature to 0.2.
Lower temperature reduces randomness, making outputs more consistent.
C
Increase candidateCount to 3.
Why wrong: More candidates do not reduce variability of a single response.
D
Increase maxOutputTokens to 2000.
Why wrong: Max tokens does not affect variability.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Decrease temperature to 0.2.
Temperature controls the randomness of token sampling. Lowering temperature (e.g., to 0.2) makes the model's output more deterministic by reducing the probability of low-likelihood tokens, thus decreasing variability across calls for the same prompt.
Key principle: Temperature
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
✗
Decrease topK to 10.
Why it's wrong here
Lower topK reduces diversity but temperature is more impactful.
✓
Decrease temperature to 0.2.
Why this is correct
Lower temperature reduces randomness, making outputs more consistent.
Clue confirmation
The clue word "most likely" in the question point toward this answer.
Related concept
Temperature
✗
Increase candidateCount to 3.
Why it's wrong here
More candidates do not reduce variability of a single response.
✗
Increase maxOutputTokens to 2000.
Why it's wrong here
Max tokens does not affect variability.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Candidates often mistake topK or candidateCount for the primary control of output variability, but in Google's Vertex AI and Gen AI models, temperature is the direct parameter that governs randomness in token selection.
Detailed technical explanation
How to think about this question
Temperature scales the logits before applying softmax: lower temperature (e.g., 0.2) sharpens the probability distribution, making high-probability tokens even more likely and low-probability tokens nearly impossible. This is distinct from topK, which truncates the candidate pool to the K most likely tokens but still allows random sampling among them. In practice, setting temperature to 0 (greedy decoding) eliminates all randomness, but a low non-zero value like 0.2 balances determinism with minimal diversity.
KKey Concepts to Remember
Temperature
TopK
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
Temperature
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. Temperature 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.
Review temperature, then practise related Generative AI Leader questions on the same topic to reinforce the concept.
What does this Generative AI Leader question test?
Techniques to Improve Generative AI Model Output — This question tests Techniques to Improve Generative AI Model Output — Temperature.
What is the correct answer to this question?
The correct answer is: Decrease temperature to 0.2. — Temperature controls the randomness of token sampling. Lowering temperature (e.g., to 0.2) makes the model's output more deterministic by reducing the probability of low-likelihood tokens, thus decreasing variability across calls for the same prompt.
What should I do if I get this Generative AI Leader question wrong?
Review temperature, then practise related Generative AI Leader questions on the same topic to reinforce the concept.
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?
Temperature
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Question Discussion
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