AI-900 Practice Question: Describe Artificial Intelligence workloads and considerations
What is 'model cards' in responsible AI and what information do they contain?
⚠ Common exam trap
Candidates often confuse operational documents (billing, hardware specs) with the transparency and accountability documentation required by responsible AI principles, leading them to select plausible-sounding but incorrect options like A or C.
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
✓
Transparency documents describing a model's intended use, training data, performance, biases, and limitations
Model cards are transparency documents that accompany machine learning models to disclose their intended use, training data, performance metrics, known biases, and limitations. They are a key responsible AI practice, mandated by frameworks like Microsoft's Responsible AI Standard, to ensure stakeholders understand a model's capabilities and risks before deployment.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Azure billing documents showing the monthly cost of running a model in production
Why it's wrong here
Model cards are not financial artifacts; Azure invoices and cost reports are operational usage records generated by Azure Cost Management. A model card instead answers questions like 'What was this model trained on?', 'Which demographic groups perform worse?', and 'What caveats should deployment teams know?' Runtime cost, reserved capacity, and monthly spend are useful for budgeting but say nothing about a model's fairness, robustness, or intended use, which are the core disclosures in a model card.
- ✓
Transparency documents describing a model's intended use, training data, performance, biases, and limitations
Why this is correct
Model cards are the responsible-AI transparency documents that accompany a trained model and summarize its intended use, training data, performance across groups, biases, and limitations. By making these details explicit, model cards let organizational reviewers decide proactively whether a model is appropriate for a specific scenario and where it may need additional testing or mitigation. This disclosure-first approach is central to Microsoft's responsible AI principles, and it is why the option is the correct definition of a model card.
- ✗
Technical specification sheets for AI hardware accelerators used in model training
Why it's wrong here
Hardware specification sheets describe physical components such as GPU memory, thermal design power, or tensor-core throughput, but they do not describe a trained model's behavior. Model cards are not device datasheets; they document the model's training recipe, validation methodology, known biases, and deployment risks. AI accelerator specifications are relevant when choosing compute for training or inference, yet they neither disclose model limitations nor enable the kind of responsible-AI scrutiny that model cards provide.
- ✗
Playing cards used in gamification of AI training to motivate data labellers
Why it's wrong here
The term model card has a precise meaning in responsible AI, and it has nothing to do with card games or gamified labeling work. Model cards are concise, standardized documents published by model developers to explain a model's intended tasks, training data, evaluation metrics, known biases, and failure modes. Gamified data-labeling incentives such as points or badges might improve labeling efficiency, but they do not serve the transparency and accountability purpose that model cards are designed for.
Go deeper
Related to this question
Learn chapter
Responsible AI Principles
Key term
Machine learning
Machine learning is a branch of artificial intelligence where computers learn patterns from data to make decisions or predictions without being explicitly programmed for every task.
Key term
Model
In IT and AI, a model is a trained mathematical representation that learns patterns from data to make predictions or decisions.
About these practice questions
This AI-900 question is part of Courseiva's 985-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 AI-900 practice question is part of Courseiva's free Microsoft 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 AI-900 exam.