AIF-C01 Guidelines for Responsible AI Practice Question
Which TWO actions are most aligned with responsible AI practices when deploying a model that makes decisions affecting individuals? (Choose 2)
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
✓
Continuously monitor the model for fairness metrics
Option B is correct because responsible AI requires ongoing monitoring of deployed models for fairness metrics (such as demographic parity, equalized odds, or disparate impact) to detect and mitigate bias that can emerge or drift after deployment. Option E is correct because providing meaningful explanations for model decisions supports transparency and accountability, enabling affected individuals to understand and potentially contest decisions, which aligns with principles like explainability and due process. Option A is incorrect because indiscriminate data collection without quality checks introduces noise, bias, and privacy risks rather than improving responsible AI. Option C is incorrect because a homogeneous development team increases the risk of blind spots and systemic bias, whereas diverse teams better surface fairness concerns. Option D is incorrect because maximizing model complexity for accuracy alone ignores interpretability, fairness, and other responsible AI trade-offs.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Collect as much data as possible without quality checks
Why it's wrong here
Collecting data indiscriminately without quality checks imports noise, duplication and historical bias into the training set, degrading decisions affecting individuals. It is tempting because larger datasets often improve accuracy, but responsible AI requires documented provenance, consent and quality validation so that model outputs remain fair and defensible.
- ✓
Continuously monitor the model for fairness metrics
Why this is correct
Fairness metrics can drift after deployment as data distributions shift, so continuous monitoring detects emerging bias against protected groups. This satisfies the responsible AI requirement for ongoing oversight of models making consequential decisions about individuals.
- ✗
Ensure the development team is homogeneous to avoid conflicts
Why it's wrong here
A homogeneous team narrows the perspectives shaping the model, entrenching bias and reducing scrutiny of decisions affecting individuals. It is tempting because shared background can speed consensus, but responsible AI requires diverse teams, bias testing and stakeholder review so that harms to different groups are identified before deployment.
- ✗
Use the most complex model available for maximum accuracy
Why it's wrong here
Selecting the most complex model maximises accuracy but sacrifices interpretability, making decisions harder to explain or contest for affected individuals. It is tempting because accuracy is a legitimate goal, yet responsible AI for consequential decisions favours explainable, auditable models and documented limitations rather than complexity for its own sake.
- ✓
Provide meaningful explanations for model decisions
Why this is correct
Individuals affected by automated decisions need to understand the basis for outcomes, so providing meaningful explanations satisfies the transparency and accountability principles of responsible AI. This enables contestability and informed recourse, which opaque model outputs would prevent.
Quick reference
RAID Level Comparison
| RAID Level | Min Disks | Fault Tolerance | Read | Write | Usable Capacity |
|---|---|---|---|---|---|
| RAID 0 | 2 | None | Excellent | Excellent | 100% |
| RAID 1 | 2 | 1 disk | Good | Moderate | 50% |
| RAID 5 | 3 | 1 disk | Good | Moderate | 67–94% |
| RAID 6 | 4 | 2 disks | Good | Lower | 50–88% |
| RAID 10 | 4 | 1 disk per mirror | Excellent | Good | 50% |
RAID is not a backup strategy — it protects against disk failure but not against accidental deletion, ransomware, or site-level events.
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JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This AIF-C01 practice question is part of Courseiva's free Amazon Web Services 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 AIF-C01 exam.