The answer is the F1-score. In highly imbalanced classification, such as a fraud detection dataset with 99% legitimate transactions and only 1% fraud, accuracy becomes a misleading metric because a model that simply predicts “legitimate” for every case would achieve 99% accuracy yet catch zero fraud cases. The F1-score solves this by calculating the harmonic mean of precision and recall, which penalizes extreme imbalances and provides a balanced view of both false positives and false negatives. On the AWS Certified AI Practitioner AIF-C01 exam, this concept tests your understanding of why standard accuracy fails for imbalanced datasets and how F1-score becomes the ideal metric for evaluating rare-event detection models. A common trap is choosing accuracy because it looks high, but the key insight is that F1-score reveals whether the model actually finds the minority class. Memory tip: think “F1 for the 1%” — when one class is rare, F1 is the fairest measure.
AIF-C01 Fundamentals of AI and ML Practice Question
This AIF-C01 practice question tests your understanding of fundamentals of ai and ml. Compare every option against the stated constraints before choosing — the best answer satisfies all requirements, not just the most obvious one. 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 data scientist is building a binary classification model for fraud detection. The dataset is highly imbalanced (99% legitimate, 1% fraud). Which metric is most appropriate to evaluate model performance?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
F1-score
In highly imbalanced datasets (99% legitimate, 1% fraud), accuracy is misleading because a model that predicts all transactions as legitimate would achieve 99% accuracy but fail to detect any fraud. The F1-score is the harmonic mean of precision and recall, providing a balanced measure that accounts for both false positives and false negatives, making it the most appropriate metric for evaluating fraud detection models.
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.
✗
Accuracy
Why it's wrong here
Accuracy is not reliable for imbalanced datasets as it can be high even if the model predicts all samples as the majority class.
✓
F1-score
Why this is correct
F1-score is the harmonic mean of precision and recall, providing a balanced evaluation for imbalanced datasets.
Related concept
Read the scenario before looking for a memorised answer.
✗
Recall
Why it's wrong here
Recall measures false negatives but ignores false positives, not ideal for imbalanced data.
✗
Precision
Why it's wrong here
Precision measures false positives but ignores false negatives, not ideal for imbalanced data.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Cisco often tests the misconception that accuracy is always the best metric, especially when candidates overlook the impact of class imbalance on model evaluation.
Detailed technical explanation
How to think about this question
The F1-score is calculated as 2 * (precision * recall) / (precision + recall), and it is particularly useful when the class distribution is skewed because it penalizes extreme imbalances between precision and recall. In fraud detection, the cost of false negatives (missing a fraud) is often high, but false positives (flagging legitimate transactions) also incur operational costs, so the F1-score provides a single metric that balances these trade-offs. Real-world systems often use the F1-score alongside precision-recall curves to tune thresholds for optimal performance.
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: F1-score — In highly imbalanced datasets (99% legitimate, 1% fraud), accuracy is misleading because a model that predicts all transactions as legitimate would achieve 99% accuracy but fail to detect any fraud. The F1-score is the harmonic mean of precision and recall, providing a balanced measure that accounts for both false positives and false negatives, making it the most appropriate metric for evaluating fraud detection models.
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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