Generative AI Leader Fundamentals of Generative AI Practice Question
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?
⚠ Common exam trap
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.
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
✓
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.
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.
- ✗
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.
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