The correct answer is that a top_p value that is too high is the most likely cause, and reducing top_p is the best parameter adjustment for fixing repetitive outputs in generative AI chat model Vertex AI. This occurs because a high top_p accumulates a large set of high-probability tokens, causing the model to repeatedly select from the same narrow pool and get stuck in a loop after a few turns. By lowering top_p, you restrict the cumulative probability threshold, forcing the model to sample from a smaller, more diverse set of tokens and breaking the repetition cycle without the global randomness introduced by temperature adjustments. On the Google Cloud Generative AI Leader exam, this scenario tests your understanding of sampling strategies versus temperature: a common trap is confusing top_p with temperature, but remember that top_p controls the nucleus of tokens considered, while temperature scales the entire probability distribution. A useful memory tip is “Top_p trims the pool, temperature tunes the whole tool.”
Generative AI Leader Fundamentals of Generative AI Practice Question
This Generative AI Leader 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.
A data scientist notices that a text generation model deployed on Vertex AI returns repetitive outputs after a few turns in a chat application. What is the most likely cause and the best parameter adjustment?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue: "best"
Why it matters: Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.
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
✓
The top_p value is too high; reduce top_p to limit token sampling.
Repetitive outputs in a chat application after a few turns are typically caused by the model getting stuck in a loop due to high cumulative probability from top-p sampling. Reducing top_p limits the set of tokens considered at each step, forcing the model to explore less likely tokens and breaking the repetition cycle. This directly addresses the issue without sacrificing coherence, unlike temperature adjustments which affect randomness globally.
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.
✗
The max_output_tokens is too low; increase it to allow more diverse output.
Why it's wrong here
Max tokens controls length, not repetition.
✓
The top_p value is too high; reduce top_p to limit token sampling.
Why this is correct
Reducing top_p narrows the token pool, reducing repetition.
Clue confirmation
The clue words "best", "most likely" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
✗
The model is overfitted; switch to a smaller model.
Why it's wrong here
Overfitting is unlikely in pre-trained models; repetition is a decoding issue.
✗
The temperature is too low; increase temperature to add randomness.
Why it's wrong here
Low temperature makes output more deterministic, increasing repetition.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Google Cloud often tests the misconception that temperature and top-p both control randomness in the same way, but the trap here is that candidates confuse 'increasing randomness' (temperature) with 'limiting the sampling pool' (top-p), leading them to choose D instead of B.
Trap categories for this question
Command / output trap
Low temperature makes output more deterministic, increasing repetition.
Detailed technical explanation
How to think about this question
Top-p (nucleus) sampling works by selecting the smallest set of tokens whose cumulative probability exceeds the threshold p, then sampling only from that set. When p is too high (e.g., 0.95), the set includes many high-probability tokens, allowing the model to repeatedly choose similar ones. Reducing p (e.g., to 0.8) narrows the set, forcing the model to consider lower-probability tokens and breaking repetitive patterns. In Vertex AI, this is a common tuning parameter for chat models like PaLM 2 or Gemini to maintain conversational flow.
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.
What does this Generative AI Leader question test?
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: The top_p value is too high; reduce top_p to limit token sampling. — Repetitive outputs in a chat application after a few turns are typically caused by the model getting stuck in a loop due to high cumulative probability from top-p sampling. Reducing top_p limits the set of tokens considered at each step, forcing the model to explore less likely tokens and breaking the repetition cycle. This directly addresses the issue without sacrificing coherence, unlike temperature adjustments which affect randomness globally.
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.
Are there clue words in this question I should notice?
Yes — watch for: "best", "most likely". Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.
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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