- A
Embedding dimension
Why wrong: Embedding dimension is a model architecture parameter, not a sampling parameter.
- B
Context window
Why wrong: Context window size determines how much prior text the model sees, not randomness.
- C
Top-k
Why wrong: Top-k limits the number of tokens considered, but randomness is primarily controlled by temperature.
- D
Temperature
Temperature directly controls the randomness of token selection.
Question 390 of 997
Generative AI Leader Generative AI Concepts and Technologies Practice Question
This Generative AI Leader practice question tests your understanding of generative ai concepts and technologies. 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 parameter controls the randomness of a language model's output?
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
✓
Temperature
Temperature is the parameter that directly controls the randomness of a language model's output by scaling the logits before applying the softmax function. A higher temperature (e.g., >1.0) makes the probability distribution more uniform, increasing randomness, while a lower temperature (e.g., <1.0) sharpens the distribution, making the model more deterministic and focused on high-probability tokens.
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.
- ✗
Embedding dimension
Why it's wrong here
Embedding dimension is a model architecture parameter, not a sampling parameter.
- ✗
Context window
Why it's wrong here
Context window size determines how much prior text the model sees, not randomness.
- ✗
Top-k
Why it's wrong here
Top-k limits the number of tokens considered, but randomness is primarily controlled by temperature.
- ✓
Temperature
Why this is correct
Temperature directly controls the randomness of token selection.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Google often tests the distinction between sampling parameters (temperature, top-k, top-p) and architectural parameters (embedding dimension, context window), so candidates may confuse top-k as a randomness control when it actually restricts the candidate pool rather than adjusting the probability distribution's entropy.
Detailed technical explanation
How to think about this question
Under the hood, temperature modifies the logits (pre-softmax scores) by dividing them by the temperature value: logits' = logits / T. A temperature of 0.7 is commonly used for creative tasks, while a temperature of 0.1 is used for factual or deterministic outputs. In practice, combining temperature with top-p (nucleus sampling) is a common technique to balance randomness and coherence, as seen in models like GPT-4 and LLaMA.
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 Generative AI Leader question test?
Generative AI Concepts and Technologies — This question tests Generative AI Concepts and Technologies — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Temperature — Temperature is the parameter that directly controls the randomness of a language model's output by scaling the logits before applying the softmax function. A higher temperature (e.g., >1.0) makes the probability distribution more uniform, increasing randomness, while a lower temperature (e.g., <1.0) sharpens the distribution, making the model more deterministic and focused on high-probability tokens.
What should I do if I get this Generative AI Leader 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
This Generative AI Leader practice question is part of Courseiva's free Google Cloud 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 Generative AI Leader exam.
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