AIF-C01 Fundamentals of AI and ML Practice Question
A company is using Amazon Fraud Detector to detect fraudulent transactions. Which TWO actions can be taken to improve model accuracy? (Select TWO.)
⚠ Common exam trap
The AIF-C01 exam often tests the misconception that changing model versions or detector types alone improves accuracy, when in reality accuracy improvements require data or feature enhancements.
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
✓
Increase the volume of event data
Option A is correct because Amazon Fraud Detector's model accuracy improves with more historical event data — a larger volume of labeled fraud/legitimate events gives the training algorithm more examples to learn patterns from, reducing overfitting and improving predictions. Option E is correct because model accuracy depends heavily on feature quality; choosing event variables with stronger predictive power (e.g., customer age, order price, IP address, email domain) directly improves the model's ability to distinguish fraudulent from legitimate transactions. Option B is incorrect because deploying a model to multiple endpoints only affects availability/scalability of predictions, not the underlying model's accuracy. Option C is incorrect because changing the detector type (e.g., online fraud, transaction fraud) alters the use case rather than inherently improving accuracy for the existing scenario. Option D is incorrect because switching to a different model version simply selects an already-trained model; it does not by itself improve accuracy unless retraining with better data or variables occurs.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Increase the volume of event data
Why this is correct
Fraud Detector's models learn from historical event data, so a larger volume of labelled fraud and legitimate events gives the algorithm more signal to distinguish patterns. This directly addresses the accuracy constraint by reducing overfitting to sparse samples.
- ✗
Deploy the model to multiple endpoints
Why it's wrong here
Deploying to multiple endpoints only increases availability and throughput; it does not alter the training data or model, so accuracy stays unchanged. It is tempting because scaling endpoints is a common operational task, and it would be correct when the requirement is higher inference capacity or resilience.
- ✗
Use a different detector type
Why it's wrong here
Changing detector type alters the fraud use case the model targets, not its accuracy on the existing one; accuracy improves through richer training data and rule tuning. It is tempting because detector type is a configurable setting, and it would be correct when the business scenario itself changes, such as moving to account takeover detection.
- ✗
Use a different model version
Why it's wrong here
Changing model version only swaps between already-trained detector versions; it does not retrain on new labelled fraud data, so accuracy cannot improve. It is tempting because version switching is a genuine deployment control for rollback or A/B comparison, which is the right choice when comparing existing versions rather than improving the underlying model.
- ✓
Select event variables that are more predictive
Why this is correct
Model accuracy depends on the discriminative power of input variables. Choosing event variables with stronger correlation to fraudulent outcomes raises the model's ability to separate classes, directly improving accuracy rather than merely adding training volume.
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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.