The answer is the F1 score. This metric is the correct choice because it is the harmonic mean of precision and recall, providing a single score that balances the trade-off between catching positive cases and avoiding false alarms—critical when the positive class is rare and accuracy is misleadingly high. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this scenario tests your understanding that high accuracy on an imbalanced dataset often masks a model that simply predicts the majority class, as seen here with 95% accuracy but only 10% recall. A common trap is to rely on AUC-ROC, which can still appear strong even with poor recall on the minority class, whereas the F1 score directly penalizes that imbalance. Remember the mnemonic: “When the positive is rare and false alarms are dear, F1 is the metric to steer.”
MLS-C01 Modeling Practice Question
This MLS-C01 practice question tests your understanding of modeling. 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 data scientist is training a binary classification model on an imbalanced dataset where the positive class is rare. The model currently achieves 95% accuracy but only 10% recall on the positive class. Which metric should the data scientist prioritize to improve model performance?
F1 score combines precision and recall, making it suitable for imbalanced datasets where both false positives and false negatives are important.
B
AUC-ROC
Why wrong: AUC-ROC may be overly optimistic on imbalanced datasets because it evaluates performance across all thresholds, often overemphasizing the majority class.
C
Precision
Why wrong: Precision alone does not consider recall; a model could have high precision but low recall, missing many positive cases.
D
Accuracy
Why wrong: Accuracy can be misleading when the positive class is rare because a model that always predicts the majority class can achieve high accuracy.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
F1 score
The F1 score is the harmonic mean of precision and recall, making it the best single metric to optimize when the positive class is rare and both false positives and false negatives are costly. With 95% accuracy but only 10% recall, the model is likely predicting the majority class almost exclusively, so improving recall without sacrificing precision is critical — the F1 score directly balances this trade-off.
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.
✓
F1 score
Why this is correct
F1 score combines precision and recall, making it suitable for imbalanced datasets where both false positives and false negatives are important.
Related concept
Read the scenario before looking for a memorised answer.
✗
AUC-ROC
Why it's wrong here
AUC-ROC may be overly optimistic on imbalanced datasets because it evaluates performance across all thresholds, often overemphasizing the majority class.
✗
Precision
Why it's wrong here
Precision alone does not consider recall; a model could have high precision but low recall, missing many positive cases.
✗
Accuracy
Why it's wrong here
Accuracy can be misleading when the positive class is rare because a model that always predicts the majority class can achieve high accuracy.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Cisco often tests the misconception that accuracy is always the best metric, but the trap here is that on imbalanced datasets, accuracy is misleadingly high even when the model fails to detect the rare positive class, so candidates must recognize that F1 score (or precision-recall AUC) is the appropriate choice.
Detailed technical explanation
How to think about this question
The F1 score ranges from 0 to 1 and is defined as 2 * (precision * recall) / (precision + recall). In imbalanced classification, threshold tuning (e.g., lowering the decision threshold from 0.5 to 0.3) can improve recall at the cost of precision, and the F1 score captures the net effect. Real-world scenarios like fraud detection or rare disease diagnosis rely on F1 because missing a positive (low recall) is often more harmful than a false alarm.
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 startup's cloud architect reviews their monthly bill and notices costs are higher than expected for a long-running batch job. Switching from on-demand instances to Reserved Instances — or using Spot/Preemptible VMs — can reduce compute costs by up to 72 %. Questions like this test whether you understand the tradeoffs between commitment, flexibility, and cost across cloud pricing models.
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.
Modeling — This question tests Modeling — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: F1 score — The F1 score is the harmonic mean of precision and recall, making it the best single metric to optimize when the positive class is rare and both false positives and false negatives are costly. With 95% accuracy but only 10% recall, the model is likely predicting the majority class almost exclusively, so improving recall without sacrificing precision is critical — the F1 score directly balances this trade-off.
What should I do if I get this MLS-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.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. A data scientist is training a binary classification model on imbalanced data (95% negative, 5% positive). Which metric is most appropriate for evaluating model performance?
easy
A.R-squared
B.Mean Squared Error (MSE)
✓ C.Area Under the ROC Curve (AUC-ROC)
D.Accuracy
Why C: AUC-ROC is the most appropriate metric for imbalanced binary classification because it evaluates the model's ability to distinguish between positive and negative classes across all classification thresholds, without being biased by the 95% negative majority. It measures the trade-off between true positive rate and false positive rate, making it robust to class imbalance.
Variation 2. A data scientist is training a binary classification model on imbalanced data (95% negative, 5% positive). The model achieves 99% accuracy on the test set but fails to detect any positive cases. Which metric should the scientist focus on to evaluate model performance?
medium
A.Accuracy
✓ B.Recall
C.RMSE
D.Precision
Why B: Option B is correct because recall (true positive rate) measures the ability to find all positive samples, which is critical for imbalanced datasets where accuracy can be misleading. Option A is wrong because accuracy is high but misleading. Option C is wrong because precision alone doesn't capture the missed positives. Option D is wrong because RMSE is for regression.
Variation 3. A data scientist is training a binary classification model on imbalanced data (95% negative, 5% positive). The model achieves 95% accuracy but only 10% recall on the positive class. Which metric should be used to evaluate model performance?
easy
✓ A.F1 score
B.Accuracy
C.Recall
D.Precision
Why A: Option C is correct because with imbalanced data, accuracy is misleading. F1 score balances precision and recall. Option A is wrong because accuracy is high but not informative. Option B is wrong because precision alone ignores recall. Option D is wrong because recall alone ignores precision.
Variation 4. A data scientist is training a binary classification model on an imbalanced dataset where the positive class represents only 1% of the data. The model achieves 99% accuracy but fails to identify most positive cases. Which metric should the data scientist use to evaluate model performance?
easy
A.R-squared
✓ B.F1 score
C.Accuracy
D.RMSE
Why B: The F1 score is the harmonic mean of precision and recall, making it ideal for imbalanced datasets where accuracy is misleading. Since the model achieves 99% accuracy by simply predicting the majority class (negative), it fails to capture positive cases; F1 score penalizes this by balancing false positives and false negatives, providing a more truthful performance measure.
Variation 5. A data scientist is training a binary classification model on an imbalanced dataset where the positive class represents 5% of the data. The model achieves 99% accuracy but only identifies 10% of the actual positive cases. Which metric should the data scientist focus on to evaluate the model's performance on the positive class?
medium
A.Precision
✓ B.Recall
C.AUC-ROC
D.F1 score
Why B: Recall measures the proportion of actual positives correctly identified, which is critical for imbalanced datasets where accuracy is misleading. Option A is wrong because precision focuses on correctness of positive predictions, not coverage. Option B is wrong because F1 balances precision and recall but doesn't directly address the low recall. Option D is wrong because AUC-ROC considers overall separability, not specifically recall of the positive class.
Variation 6. A data scientist is training a binary classification model on an imbalanced dataset where the positive class represents 5% of the data. Which metric is most appropriate for evaluating model performance?
easy
A.Accuracy
✓ B.AUC-ROC
C.Root Mean Squared Error (RMSE)
D.R-squared
Why B: Option B is correct because AUC-ROC is robust to class imbalance and measures the trade-off between true positive rate and false positive rate. Option A is wrong because accuracy can be misleading with imbalanced data. Option C is wrong because RMSE is for regression. Option D is wrong because R-squared is for regression.
Variation 7. A data scientist is training a binary classification model on an imbalanced dataset where the positive class represents only 5% of the data. The model currently achieves 95% accuracy but only 10% recall on the positive class. Which metric should the scientist focus on to improve the model's ability to detect the positive class?
medium
✓ A.Recall
B.Accuracy
C.Precision
D.AUC-ROC
Why A: Option B is correct because recall measures the ability to find all positive samples, which is the key issue in this imbalanced dataset. Option A is wrong because accuracy is misleading when classes are imbalanced. Option C is wrong because precision does not directly address the low recall. Option D is wrong because AUC-ROC is a global metric that may not reflect the improvement in recall.
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This MLS-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 MLS-C01 exam.
Question Discussion
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