AIF-C01 Guidelines for Responsible AI Practice Question
A financial services company uses a machine learning model to automatically reject credit card transactions suspected of fraud. The model was trained on transaction data from the past two years. Over the last three months, the model's false positive rate has increased significantly, causing legitimate transactions to be declined and leading to customer complaints. The company needs to restore the model's accuracy quickly. Initial analysis shows that the distribution of transaction amounts and locations has shifted compared to the training period. The data science team is under pressure to deploy an update within a week. Which approach should they take to most effectively address the issue while adhering to responsible AI guidelines?
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
✓
Retrain the model using only the most recent three months of transaction data and evaluate on current distribution
The most effective approach is to retrain the model using recent data (last three months) to adapt to the distribution shift, and carefully evaluate for any new biases that may emerge. This directly addresses the drift. Simply adjusting the threshold may not capture new fraud patterns. Using an ensemble of old and recent models could be complex and may not fully adapt. Deploying a simple rule-based system would be a step backward in capability.
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 a rule-based system with fixed rules for fraud detection
Why it's wrong here
Rule-based systems lack adaptability and may not capture complex fraud patterns.
- ✗
Adjust the decision threshold to reduce false positives without retraining
Why it's wrong here
Threshold adjustment may not address the root cause of drift and could miss fraud.
- ✓
Retrain the model using only the most recent three months of transaction data and evaluate on current distribution
Why this is correct
Retraining on recent data adapts to drift and is straightforward.
- ✗
Build an ensemble model that combines predictions from the old model and a new model trained on recent data
Why it's wrong here
Ensembles require more time and may not stabilize quickly.
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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.