- A
Mean squared error
Why wrong: MSE is for regression problems.
- B
Accuracy
Why wrong: Accuracy is misleading for imbalanced datasets.
- C
Recall
Recall measures the proportion of actual positives correctly identified.
- D
Precision
Why wrong: Precision measures the proportion of predicted positives that are actual positives.
Question 896 of 1,755
Why Recall Matters for Imbalanced Classification on AWS MLS
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 a highly imbalanced dataset where the positive class represents only 1% of the data. The model achieves 99% accuracy but only identifies 5% of the actual positives. Which metric should the data scientist use to evaluate model performance?
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 by the model. With only 5% of positives detected, recall is 0.05, which directly reveals the model's failure to capture the minority class despite high accuracy. In imbalanced datasets, accuracy is misleading because the model can achieve 99% accuracy by simply predicting the majority class (negative) for all instances.
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.
- ✗
Mean squared error
Why it's wrong here
MSE is for regression problems.
- ✗
Accuracy
Why it's wrong here
Accuracy is misleading for imbalanced datasets.
- ✓
Recall
Why this is correct
Recall measures the proportion of actual positives correctly identified.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Precision
Why it's wrong here
Precision measures the proportion of predicted positives that are actual positives.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The MLS-C01 exam often tests the trap that high accuracy implies good performance on imbalanced datasets, leading candidates to choose accuracy without considering class distribution or the specific failure mode (low recall).
Detailed technical explanation
How to think about this question
Recall is calculated as TP / (TP + FN). In this scenario, with only 1% positives, even a model that predicts all negatives yields TP=0, FN=all positives, recall=0. The confusion matrix reveals that high accuracy masks poor recall; for example, with 10,000 samples (100 positives), the model might correctly identify 5 positives (TP=5) and miss 95 (FN=95), giving recall=0.05. In production, low recall can be catastrophic in domains like fraud detection or disease screening where missing positives has high cost.
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.
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FAQ
Questions learners often ask
What does this MLS-C01 question test?
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: Recall — Recall (sensitivity) measures the proportion of actual positives correctly identified by the model. With only 5% of positives detected, recall is 0.05, which directly reveals the model's failure to capture the minority class despite high accuracy. In imbalanced datasets, accuracy is misleading because the model can achieve 99% accuracy by simply predicting the majority class (negative) for all instances.
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 →
Same concept, more angles
2 more ways this is tested on MLS-C01
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 a highly imbalanced dataset (99% negative class, 1% positive class). The model currently achieves 99% accuracy but only identifies 0.5% of true positives. Which metric should the data scientist focus on to improve model performance?
easy- A.Precision
- B.Root Mean Squared Error (RMSE)
- ✓ C.Recall
- D.Accuracy
Why C: Recall (sensitivity) measures the proportion of actual positives correctly identified, which is critical when the dataset is highly imbalanced (99% negative, 1% positive) and the model fails to detect most positives (only 0.5% true positives). Improving recall directly addresses the model's inability to capture the minority class, even if it reduces precision or accuracy. In binary classification with severe class imbalance, accuracy is misleading because a model can achieve 99% accuracy by simply predicting the majority class, as seen here.
Variation 2. A data scientist is training a binary classifier on an imbalanced dataset (95% negative, 5% positive). The model achieves 99% accuracy but only correctly identifies 2% of the positive samples. Which metric should the data scientist focus on to improve the model's performance?
easy- A.Precision
- B.RMSE
- ✓ C.Recall
- D.Accuracy
Why C: The correct answer is C (Recall). Recall measures the proportion of actual positive samples correctly identified, which is critical for an imbalanced dataset where the model fails to detect positives. Option A (Precision) is not the primary focus because it measures the accuracy of positive predictions, not the ability to find all positives. Option B (RMSE) is a regression metric, not suitable for binary classification. Option D (Accuracy) is misleading because a model can achieve high accuracy by simply predicting the majority class, as seen here.
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Last reviewed: Jun 24, 2026
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