hardMultiple Choice
Generative AI Leader Practice Question: An AI research lab wants to publish a model card…
An AI research lab wants to publish a model card for their new generative AI model. According to Google's Responsible AI practices, which information is ESSENTIAL to include in the model card?
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 intended use, performance metrics, and known limitations
Model Cards should disclose intended use, performance metrics, and known limitations to promote transparency and accountability.
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 exact architecture and hyperparameters used
Why it's wrong here
Model cards summarise intended use, limitations, evaluation data and fairness metrics; exact architecture and hyperparameters are engineering artefacts, not responsible-AI disclosures. It is tempting because reproducibility benefits from such detail, and technical reports do publish it, but the model card's purpose is transparency about behaviour and risk, not implementation specification.
- ✓
The intended use, performance metrics, and known limitations
Why this is correct
Intended use, performance metrics and known limitations form the core of Google's model card framework, satisfying the stem's requirement for essential responsible-AI disclosure. These three sections address the model's purpose, measured behaviour and boundaries, letting downstream users judge suitability and risk before deployment.
- ✗
A list of all training hardware and software versions
Why it's wrong here
Hardware and software versions are reproducibility details, not the responsible-AI content Google's model card guidance requires. Model cards must document intended use, limitations, and evaluation results across demographic groups. Listing training infrastructure would be appropriate in a technical system card or reproducibility checklist instead.
- ✗
The names and salaries of the development team
Why it's wrong here
Model cards document intended use, limitations, evaluation metrics and ethical considerations, not personnel remuneration. Publishing salary data breaches employee privacy and reveals nothing about model behaviour, bias or performance. It is tempting because team transparency and accountability matter in Responsible AI, but headcount details belong in internal project documentation, not a public model card.
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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.