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Generative AI Leader Practice Question: Commercially use images generated by a…
A company plans to commercially use images generated by a text-to-image model. What should they check to avoid copyright issues?
⚠ Common exam trap
Google often tests the misconception that technical performance metrics (accuracy, latency, bias) are relevant to legal compliance, when in fact copyright issues hinge on data provenance and licensing.
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 training data provenance and the model's license terms
Copyright issues arise from the training data and the model's license. The training data provenance determines if the model was trained on copyrighted works without permission, and the license terms specify whether commercial use is allowed. Without checking both, the company risks infringing on original creators' rights.
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 training data provenance and the model's license terms
Why this is correct
Commercial reuse depends on the model's licence and the provenance of its training images, since these determine whether outputs can be lawfully exploited. Checking both satisfies the stem's copyright constraint before any generated image is published or sold.
- ✗
The model's accuracy on standard benchmarks
Why it's wrong here
Benchmark accuracy measures image quality or prompt fidelity, revealing nothing about whether training data or outputs infringe third-party rights. Benchmarks suit model selection and capability comparison. Copyright clearance requires reviewing the training-data provenance and the licence terms attached to the model and its outputs.
- ✗
The model's latency and throughput
Why it's wrong here
Latency and throughput are serving-performance metrics, unrelated to intellectual-property exposure. They suit capacity planning and infrastructure sizing. Avoiding copyright issues requires examining the model's training-data provenance and the licence governing commercial use of generated images.
- ✗
The model's bias evaluation results
Why it's wrong here
Bias evaluation addresses fairness across demographic groups, not whether training images or outputs reproduce protected creative works. It suits responsible-AI review of discriminatory outcomes. Copyright risk requires checking training-data provenance and the licence terms permitting commercial use of generated images.
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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.