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AIF-C01 Practice Question: A data scientist trains a binary classification…
A data scientist trains a binary classification model and obtains the following results on the test set: accuracy 0.92, precision 0.90, recall 0.85, F1 0.87. The dataset has 5% positive class. The business requirement is to minimize false negatives. Which metric should the team prioritize?
⚠ Common exam trap
For the AWS AI Practitioner exam, recall is the key metric when minimizing false negatives is the business requirement. Accuracy can be misleading in imbalanced datasets like this one (5% positives). Candidates often pick accuracy out of habit, ignoring the specific business need.
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
✓
Recall
Recall (sensitivity) measures the proportion of actual positives correctly identified, which directly addresses the business requirement to minimize false negatives. With a 5% positive class, accuracy is misleadingly high because the model can simply predict the majority class (negative) and still achieve 95% accuracy, but this would result in zero recall. Prioritizing recall ensures the model captures as many true positives as possible, reducing false negatives at the cost of potentially more false positives.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
F1 score
Why it's wrong here
F1 balances precision and recall; if false negatives are the priority, recall alone should be optimized.
- ✗
Accuracy
Why it's wrong here
Accuracy counts all correct predictions, so with only 5% positives a model predicting everything negative scores about 0.95 while missing every true positive. It is tempting because accuracy is the default headline metric, and it is appropriate when classes are balanced and errors carry equal cost.
- ✗
Precision
Why it's wrong here
Precision measures how many predicted positives are truly positive, so raising it reduces false positives while leaving false negatives unaddressed. It is tempting because precision matters when false alarms are costly, such as flagging legitimate transactions as fraud.
- ✓
Recall
Why this is correct
Recall measures the proportion of actual positives correctly identified, so maximising it directly minimises false negatives. With only 5% positives and a business requirement to avoid missed detections, recall is the metric the team must prioritise over accuracy or precision.
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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.