Quick Answer
The answer is recall. In an imbalanced dataset like fraud detection where 99% of transactions are legitimate, a model can achieve 99% accuracy simply by predicting “legitimate” for every case, which explains why the logistic regression model fails to detect most fraud cases. Recall, also known as true positive rate or sensitivity, specifically measures the proportion of actual positive cases (fraud) that the model correctly identifies, making it the essential metric when the cost of missing a positive is high. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this scenario tests your understanding that accuracy is misleading for imbalanced datasets, and questions often present a high-accuracy but low-recall model as a common trap. A useful memory tip: when false negatives are costly, “recall” the positives—think of it as “catching the rare ones.”
This MLS-C01 practice question tests your understanding of modeling. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. 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 team is deploying a model for fraud detection. The dataset is highly imbalanced (99% legitimate, 1% fraudulent). They trained a logistic regression model and achieved 99% accuracy on the test set. However, the model fails to detect most fraud cases. Which metric should the team focus on to evaluate the model?
Mean squared error
Why wrong: MSE is for regression, not classification.
Precision
Why wrong: Precision could be high if the model only flags obvious fraud, missing many.
Recall
Recall measures the proportion of actual fraud cases correctly identified.
Accuracy
Why wrong: Accuracy is high due to class imbalance but does not reflect fraud detection.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Recall
Accuracy is misleading for imbalanced datasets. Recall (true positive rate) measures how well the model detects fraud. Option A is wrong because accuracy is already high but misleading. Option C is wrong because precision may be high but recall low. Option D is wrong because mean squared error is for regression.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
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, not classification.
Precision
Why it's wrong here
Precision could be high if the model only flags obvious fraud, missing many.
Recall
Why this is correct
Recall measures the proportion of actual fraud cases correctly identified.
Related concept
Read the scenario before looking for a memorised answer.
Accuracy
Why it's wrong here
Accuracy is high due to class imbalance but does not reflect fraud detection.
Common exam traps
Many certification questions include familiar terms but test a specific constraint. Read the exact wording before choosing an answer that is generally true but wrong for this case.
Detailed technical explanation
This question should be treated as a scenario, not a definition check. Identify the problem, the constraint and the best action. Then compare each option against those facts.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
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
Identify which MLS-C01 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
Modeling — study guide chapter
Learn the concepts, then practise the questions
Modeling practice questions
Targeted practice on this topic area only
All MLS-C01 questions
1,755 questions across all exam domains
AWS Certified Machine Learning Specialty MLS-C01 study guide
Full concept coverage aligned to exam objectives
MLS-C01 practice test guide
How to use practice tests most effectively before exam day
Related practice questions
Use these pages to review the topic behind this question. This is how one missed question becomes focused revision.
Practise MLS-C01 questions linked to Data Engineering.
Practise MLS-C01 questions linked to Machine Learning Implementation and Operations.
Practise MLS-C01 questions linked to Modeling.
Practise MLS-C01 questions linked to Exploratory Data Analysis.
Practise MLS-C01 questions linked to MLS-C01 fundamentals.
Practise MLS-C01 questions linked to MLS-C01 scenario.
Practise MLS-C01 questions linked to MLS-C01 troubleshooting.
Practice this exam
Short sessions build daily habit. Longer sessions build exam-day stamina. Try a timed session to simulate real conditions.
FAQ
Modeling — This question tests Modeling — Read the scenario before looking for a memorised answer..
The correct answer is: Recall — Accuracy is misleading for imbalanced datasets. Recall (true positive rate) measures how well the model detects fraud. Option A is wrong because accuracy is already high but misleading. Option C is wrong because precision may be high but recall low. Option D is wrong because mean squared error is for regression.
Identify which MLS-C01 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
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 →
Keep practising
Last reviewed: Jun 20, 2026
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
Share a tip, memory trick, or ask about the reasoning behind this question. Do not post real exam questions, leaked content, braindumps, or copyrighted exam material. Comments are moderated and may be removed without notice.
Sign in to join the discussion.