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AIF-C01 Practice Question: Developing a generative AI application for…
A company is developing a generative AI application for content creation. They want to ensure transparency as per responsible AI guidelines. Which THREE practices should they implement? (Choose three.)
⚠ Common exam trap
AIF-C01 often tests the confusion between transparency practices (disclosure, documentation) and fairness practices (bias monitoring), causing candidates to select bias monitoring as a transparency answer.
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
✓
Label AI-generated content with a clear disclosure
Option B is correct because labeling AI-generated content with a clear disclosure directly supports transparency, allowing users to know when content was produced by a generative AI system rather than a human. Option C is correct because providing a disclaimer about the model's capabilities and limitations helps users understand what the system can and cannot reliably do, which is a core responsible AI transparency practice. Option E is correct because documenting the training data sources and potential biases makes the model's provenance and known risks visible to stakeholders, supporting accountability and informed use. Option A is not appropriate because encouraging unquestioning trust undermines transparency and responsible AI principles. Option D, while a valid responsible AI practice for fairness, focuses on ongoing bias monitoring rather than the transparency-specific disclosure and documentation requirements described in the scenario.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Encourage users to trust the AI outputs without question
Why it's wrong here
Transparency requires disclosing AI involvement and limitations, not suppressing scrutiny; blind trust conceals the model's uncertainty and provenance. It is tempting because user confidence aids adoption, and trust-building is legitimate for change management — but responsible AI demands informed trust through disclosure, not unquestioning acceptance.
- ✓
Label AI-generated content with a clear disclosure
Why this is correct
Labelling AI-generated content with a clear disclosure directly satisfies the transparency requirement by informing end users that output is machine-generated. Disclosure prevents audiences from mistaking synthetic material for human-authored work, which is a core responsible AI transparency practise for generative content creation applications.
- ✓
Provide a disclaimer about the model's capabilities and limitations
Why this is correct
Publishing a disclaimer about the model's capabilities and limitations sets accurate user expectations, satisfying the transparency requirement. It clarifies what the generative system can and cannot reliably produce, so consumers understand output boundaries rather than over-trusting results, which is a recognised responsible AI transparency practise.
- ✗
Monitor the model for bias in production
Why it's wrong here
Bias monitoring addresses fairness and harm detection, not transparency; it reveals nothing to users about how outputs are generated. It is tempting because bias monitoring is a genuine responsible AI practise, and it would be correct when the requirement is ongoing fairness assurance rather than disclosing model behaviour and data usage.
- ✓
Document the training data sources and potential biases
Why this is correct
Documenting training data sources and potential biases exposes how the generative model was built and where its outputs may be skewed. This satisfies transparency by making provenance and known limitations auditable, letting stakeholders assess reliability and fairness instead of treating the model as an opaque black box.
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Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Amazon Web Services exam blueprint
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