Generative AI Leader Fundamentals of Generative AI Practice Question
A company is using Vertex AI to generate marketing copy. They notice that the output sometimes contains factual inaccuracies. Which parameter adjustment is most likely to improve factual accuracy?
⚠ Common exam trap
Google Cloud often tests the misconception that increasing output length or diversity (via top_p or max_tokens) improves quality, when in fact these parameters increase randomness and the likelihood of hallucination, whereas lowering temperature is the direct lever for factual accuracy.
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
✓
Decrease the temperature parameter.
Decreasing the temperature parameter reduces the randomness of the model's output, making it more deterministic and less likely to generate creative but factually incorrect content. Lower temperature (e.g., 0.1) forces the model to choose higher-probability tokens, which aligns with more factual and consistent responses, especially in tasks like marketing copy where accuracy is critical.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Decrease the temperature parameter.
Why this is correct
Lower temperature reduces randomness, making output more factual.
- ✗
Increase the max_output_tokens parameter.
Why it's wrong here
Length does not affect factual accuracy.
- ✗
Increase the top_p parameter.
Why it's wrong here
Higher top_p increases diversity, which can reduce accuracy.
- ✗
Add a post-processing step to verify facts using a database.
Why it's wrong here
This is not a parameter adjustment.
Go deeper
Related to this question
About these practice questions
This Generative AI Leader question is part of Courseiva's 683-question bank — original exam-style content with full explanations and wrong-answer analysis, never real exam questions or exam dumps. Learn why practice questions differ from exam dumps →
JA
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.