Generative AI Leader Practice Question: Techniques to Improve Generative AI Model Output
A data science team is fine-tuning a large language model using Vertex AI to generate marketing copy. They notice that the generated text is often repetitive and lacks creativity. Which technique should they apply to improve output diversity?
⚠ Common exam trap
Google Cloud often tests the misconception that decreasing sampling thresholds (like top-k or beam width) increases diversity, when in fact they reduce the candidate pool and make output more deterministic.
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 the temperature parameter to 0.9.
Increasing the temperature parameter to 0.9 raises the randomness of the probability distribution over tokens, allowing less likely tokens to be selected. This directly counteracts repetitive output by encouraging the model to explore more diverse word choices, which is a standard technique for improving creativity in text 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 the temperature parameter to 0.9.
Why this is correct
Higher temperature increases randomness and diversity in generated text.
- ✗
Decrease the beam search width to 1.
Why it's wrong here
Beam search width of 1 is greedy decoding, which reduces diversity.
- ✗
Decrease the top-k sampling threshold.
Why it's wrong here
Decreasing top-k (smaller k) reduces the pool of candidates, decreasing diversity.
- ✗
Add more examples of repetitive text to the training dataset.
Why it's wrong here
Adding more repetitive examples would likely worsen the issue.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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.