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AIF-C01 Practice Question: A financial institution is developing a model to…
A financial institution is developing a model to detect fraudulent transactions. They want to ensure the model is robust and does not exhibit bias. Which TWO actions should they take?
⚠ Common exam trap
The trap is selecting security or threshold-tuning options (C, D) that sound responsible but do not address bias — candidates must distinguish fairness actions from general best practices.
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
✓
Use Amazon SageMaker Clarify to compute fairness metrics like demographic parity
Option A is correct because Amazon SageMaker Clarify is the AWS service specifically designed to detect bias in data and models, and it computes fairness metrics such as demographic parity, disparate impact, and equal opportunity across sensitive groups, directly addressing the requirement to ensure the model does not exhibit bias. Option E is correct because a balanced training dataset that represents all transaction types and customer demographics reduces sampling bias and prevents the model from overfitting to majority groups, which is a foundational step for building a robust and fair fraud-detection model. Option B is incorrect because using a single unsegmented model for all customers can mask region-specific fraud patterns and amplify bias, rather than mitigate it. Option C is incorrect because raising the confidence threshold only tunes precision/recall trade-offs for false positives; it does nothing to detect or reduce bias. Option D is incorrect because encryption at rest and in transit is a security control for data protection and compliance, not a fairness or bias-mitigation measure.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Use Amazon SageMaker Clarify to compute fairness metrics like demographic parity
Why this is correct
SageMaker Clarify quantifies bias by computing fairness metrics such as demographic parity across groups, exposing disparities the institution could otherwise miss. Measuring these metrics lets the team detect and remediate bias before deployment, satisfying the robustness requirement.
- ✗
Use a single model for all customers without segmenting by region
Why it's wrong here
A single unsegmented model pools heterogeneous regional behaviour, so it can learn majority-region patterns and disadvantage minority segments, worsening bias. It is tempting because one model is cheaper to train, deploy and maintain. Robust, unbiased fraud detection typically requires segment-aware features, per-region evaluation and stratified sampling to expose subgroup performance gaps.
- ✗
Deploy the model with a high confidence threshold to reduce false positives
Why it's wrong here
A confidence threshold only tunes precision versus recall; it neither improves robustness to distribution shift nor removes bias encoded in features or training data. It is tempting because raising the threshold cuts false positives, which matters operationally. Bias mitigation requires representative data, fairness metrics and reweighting or resampling, not threshold tuning.
- ✗
Encrypt all transaction data at rest and in transit
Why it's wrong here
Encryption at rest and in transit protects data confidentiality against interception or theft; it does nothing about model bias or robustness to adversarial or drifting inputs. It is tempting because encryption is a standard financial-services control and appears security-relevant. Bias and robustness are addressed through data quality, fairness testing and validation, not cryptographic protection.
- ✓
Collect a balanced training dataset representing all transaction types and customer demographics
Why this is correct
Fraudulent transactions are rare, so an imbalanced dataset skews the model toward the majority class and underrepresents certain demographics. Sampling all transaction types and customer groups proportionally reduces this bias, improving robustness across the populations the institution serves.
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 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
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.