AIF-C01 Fundamentals of Generative AI Practice Question

Which TWO practices help ensure responsible AI when deploying generative AI applications? (Select TWO.)

⚠ Common exam trap

AIF-C01 often tests whether candidates confuse 'model performance improvements' (size, accuracy) with 'responsible AI controls' (guardrails, monitoring, transparency), so options that sound like optimization tricks are the trap.

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

✓

Implement guardrails to filter harmful or inappropriate content

Option D is correct because implementing guardrails—such as content filters, prompt shields, and safety classifiers—directly mitigates the risk of generative AI producing harmful, offensive, or inappropriate content, which is a core requirement of responsible AI deployment. Option E is correct because continuously monitoring model outputs for bias and drift ensures that the system remains fair and accurate as data distributions and user behavior change over time, enabling timely remediation. Option A is incorrect because removing content filters increases the risk of harmful outputs and violates responsible AI principles rather than ensuring them. Option B is incorrect because increasing model size at the cost of interpretability reduces transparency and explainability, which are key responsible AI goals. Option C is incorrect because using only synthetic data does not by itself guarantee privacy or fairness and can introduce its own biases and quality issues.

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

✗
Deploy the model without any content filters to maximize creativity
Why it's wrong here
Removing content filters lets the model emit harmful, unsafe or non-compliant output, directly violating responsible AI requirements. Filters are tempting to disable when they block legitimate creative prompts, yet they are the correct choice precisely for open-ended creative tools where misuse and harmful generation must be constrained.
✗
Increase model size to improve accuracy at the expense of interpretability
Why it's wrong here
Scaling parameters trades transparency for marginal accuracy, and interpretability is a responsible AI requirement, not an optional sacrifice. Larger models are the right choice when raw predictive accuracy on a benchmark is the sole objective and explainability is not mandated by regulation or stakeholder need.
✗
Use only synthetic data for training to avoid privacy issues
Why it's wrong here
Synthetic-only training discards real-world distribution, producing models that generalise poorly and still risk leakage if generation is flawed. Synthetic data is genuinely useful for augmenting scarce or sensitive datasets, but responsible deployment requires provenance controls, consent and evaluation on representative real data.
✓
Implement guardrails to filter harmful or inappropriate content
Why this is correct
Guardrails intercept prompts and responses, blocking harmful, inappropriate or policy-violating content before it reaches users. This enforces responsible AI by constraining generative output at runtime, satisfying the requirement to prevent unsafe material being surfaced in deployed applications.
✓
Monitor the model's outputs for bias and drift over time
Why this is correct
Continuous output monitoring detects bias and drift as data distributions and usage evolve, enabling remediation before harm accumulates. This satisfies responsible AI by verifying the deployed model remains fair and accurate over time, which static pre-deployment testing cannot guarantee.

Go deeper

AIF-C01 Fundamentals of Generative AI Practice Question

Why each option matters

Option-by-option breakdown

Related to this question