Generative AI Leader Practice Question: Business Strategies for Generative AI Solutions
A marketing agency wants to generate images using Imagen on Vertex AI. They need to ensure the images are unique and avoid copyright issues. Which parameter adjustment is most relevant?
⚠ Common exam trap
Google Cloud often tests the distinction between safety filters (which block harmful content) and negative prompts (which control stylistic or conceptual exclusion), leading candidates to mistakenly choose safety threshold adjustments for copyright avoidance.
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
✓
Use negative prompts
Negative prompts allow the model to exclude specific concepts, styles, or elements from generated images, directly reducing the risk of replicating copyrighted or trademarked content. By explicitly telling Imagen what not to include, the agency can steer outputs away from protected works without needing to modify training data or safety filters.
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 training steps
Why it's wrong here
Not applicable to inference-time generation.
- ✗
Increase seed variability
Why it's wrong here
Increases output diversity but doesn't avoid copyright.
- ✓
Use negative prompts
Why this is correct
Specifies elements to avoid, reducing copyright risk.
- ✗
Set safety threshold
Why it's wrong here
Filters offensive content, not copyright.
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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.