hardMultiple Choice
Generative AI Leader Practice Question: A music streaming service wants to use…
A music streaming service wants to use AI-generated playlists and artwork, but is concerned about potential copyright infringement. They plan to use a generative model that was trained on a large corpus of publicly available music and images. Which action is MOST important to mitigate IP risk?
⚠ Common exam trap
Generative AI Leader often tests the misconception that using a cloud provider's model shifts liability, or that watermarking solves IP issues, when the core issue is the training data's legal status.
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
✓
Review the training data provenance and ensure it consists of properly licensed or public domain works
Reviewing the training data provenance and ensuring it consists of properly licensed or public domain works is the most important action to mitigate IP risk. This directly addresses the root cause: if the model was trained on copyrighted material without permission, the outputs could infringe. By verifying that the training data is legally usable, the service reduces the risk of generating infringing content.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Review the training data provenance and ensure it consists of properly licensed or public domain works
Why this is correct
Reviewing training data provenance directly addresses the copyright exposure: a model trained on unlicensed music and images can reproduce protected expression, so verifying that the corpus comprises properly licensed or public domain works removes the infringement risk at its source. This satisfies the stem's constraint of mitigating IP risk before deployment.
- ✗
Only use models hosted on Google Cloud, as Google assumes liability
Why it's wrong here
Hosting on Google Cloud does not transfer IP liability for outputs; Google's terms allocate responsibility to the customer for how models are used and what is generated. Cloud hosting is the right consideration for data residency, security and compliance controls, but it does not resolve copyright exposure in training data or outputs.
- ✗
Add a watermark to all generated content using SynthID
Why it's wrong here
SynthID watermarks mark content as AI-generated; it does not establish copyright clearance or prevent infringement of existing works. Watermarking is the correct control for provenance, disclosure and detecting synthetic media, but the stem's risk is IP infringement arising from training data and outputs, which requires licensing or indemnified models.
- ✗
Ask the model to self-certify that its outputs are original
Why it's wrong here
A model cannot reliably self-certify originality; it has no mechanism to compare outputs against copyrighted works or training sources. Self-certification prompts are useful for stylistic or formatting constraints, but originality assurance requires documented training-data provenance, licensing review or an indemnified model.
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JA
Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Google Cloud exam blueprint
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.