The answer is selecting event variables that are more predictive and increasing the volume of event data. More predictive variables directly improve model accuracy by providing stronger signals that distinguish fraudulent from legitimate transactions, while a larger dataset gives Amazon Fraud Detector more examples of both classes, enabling the model to learn robust patterns and reduce overfitting. On the AWS Certified AI Practitioner AIF-C01 exam, this topic tests your understanding of feature engineering and data volume as core levers for model improvement—a common trap is focusing on tuning model hyperparameters instead of data quality and quantity. Remember that fraud detection models thrive on signal-rich features and abundant examples; a useful memory tip is “better data beats better algorithms,” so prioritize predictive variables and data volume before adjusting thresholds.
AIF-C01 Fundamentals of AI and ML Practice Question
This AIF-C01 practice question tests your understanding of fundamentals of ai and ml. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
A company is using Amazon Fraud Detector to detect fraudulent transactions. Which TWO actions can be taken to improve model accuracy? (Select TWO.)
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
Increasing the volume of event data provides Amazon Fraud Detector with more examples of both fraudulent and legitimate transactions, which allows the model to learn more robust patterns and reduce overfitting. More data helps the model generalize better to unseen events, directly improving prediction accuracy.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
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
More data can help the model learn better patterns.
Related concept
Read the scenario before looking for a memorised answer.
✗
Deploy the model to multiple endpoints
Why it's wrong here
Scaling endpoints improves availability and throughput, not accuracy.
✗
Use a different detector type
Why it's wrong here
Detector type (e.g., online fraud vs. registration fraud) should match the use case; changing it does not inherently improve accuracy.
✗
Use a different model version
Why it's wrong here
Model versions contain the same algorithm; changing version does not guarantee improvement.
✓
Select event variables that are more predictive
Why this is correct
Choosing relevant features improves the model's predictive power.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Cisco often tests the misconception that changing model versions or detector types alone improves accuracy, when in reality accuracy improvements require data or feature enhancements.
Detailed technical explanation
How to think about this question
Amazon Fraud Detector uses supervised learning models that rely on historical event data and selected event variables. The model's accuracy is heavily dependent on the quality and quantity of training data; more data reduces variance and helps the model capture rare fraud patterns. Selecting more predictive event variables (e.g., transaction amount, IP geolocation, device fingerprint) directly improves the signal-to-noise ratio, enabling the model to distinguish fraud more effectively.
KKey Concepts to Remember
Read the scenario before looking for a memorised answer.
Find the constraint that changes the correct option.
Eliminate answers that are true in general but not in this case.
TExam Day Tips
→Watch for words such as best, first, most likely and least administrative effort.
→Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
What to study next
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Fundamentals of AI and ML — This question tests Fundamentals of AI and ML — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Increase the volume of event data — Increasing the volume of event data provides Amazon Fraud Detector with more examples of both fraudulent and legitimate transactions, which allows the model to learn more robust patterns and reduce overfitting. More data helps the model generalize better to unseen events, directly improving prediction accuracy.
What should I do if I get this AIF-C01 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Question Discussion
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