Generative AI Leader Practice Question: Techniques to Improve Generative AI Model Output
A developer is tuning a text-generation model for creative writing. They want the outputs to be more diverse and less repetitive. Which THREE parameters/changes can help? (Choose three.)
⚠ Common exam trap
Google Cloud often tests the misconception that reducing top-k or top-p increases diversity, when in fact narrowing the sampling pool (lower top-k or lower top-p) reduces diversity, and the correct approach is to increase these values or increase temperature/penalties.
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
✓
Increase temperature to 0.9
Increasing temperature to 0.9 raises the randomness of the probability distribution over the vocabulary, making the model more likely to sample less probable tokens. This directly increases output diversity and reduces repetitiveness by flattening the softmax curve, which is a standard technique for creative generation.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Increase temperature to 0.9
Why this is correct
Higher temperature increases randomness and diversity.
- ✗
Reduce top-k to 10
Why it's wrong here
Lower top-k restricts token pool, reducing diversity.
- ✓
Increase presence penalty to 0.5
Why this is correct
Presence penalty discourages repetition, promoting varied content.
- ✓
Increase top-p to 0.95
Why this is correct
Higher top-p includes more low-probability tokens, enhancing diversity.
- ✗
Reduce frequency penalty to 0.0
Why it's wrong here
Lower frequency penalty reduces the penalty on repeated phrases, increasing repetition.
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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.