Quick Answer
The answer is accuracy, precision, and recall. These three metrics are the most commonly used for evaluating binary classification models because they provide a balanced view of performance by measuring overall correctness, the cost of false positives, and the ability to capture all positive instances. Precision specifically quantifies the proportion of true positive predictions among all positive predictions, making it critical when false positives are costly, such as in fraud detection or spam filtering. On the AWS Certified Machine Learning Specialty MLS-C01 exam, you will often see questions that test your understanding of when to prioritize precision over recall, especially in imbalanced datasets where accuracy alone can be misleading. A common trap is choosing F1-score as a primary metric instead of understanding that precision and recall are the foundational components. To remember this trio, think of the mnemonic “APR” — Accuracy, Precision, Recall — and that precision asks “were you right when you said yes?” while recall asks “did you catch all the yeses?”
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 building a binary classifier and wants to evaluate model performance. Which THREE metrics are most commonly used?
Mean Absolute Error
Why wrong: Regression metric.
RMSE
Why wrong: Regression metric.
Precision
Common classification metric.
Recall
Common classification metric.
Accuracy
Common classification metric.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Precision
Precision is a core metric for binary classifiers, measuring the proportion of true positive predictions among all positive predictions. It is especially important when the cost of false positives is high, such as in spam detection or fraud alert systems.
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 Absolute Error
Why it's wrong here
Regression metric.
RMSE
Why it's wrong here
Regression metric.
Precision
Why this is correct
Common classification metric.
Related concept
Read the scenario before looking for a memorised answer.
Recall
Why this is correct
Common classification metric.
Related concept
Read the scenario before looking for a memorised answer.
Accuracy
Why this is correct
Common classification metric.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
AWS often tests the distinction between regression and classification metrics, and the trap here is that candidates mistakenly apply regression metrics like MAE or RMSE to binary classification problems.
Detailed technical explanation
Precision is calculated as TP/(TP+FP) and is part of the confusion matrix evaluation for classification models. In imbalanced datasets, precision becomes critical because a model can achieve high accuracy by simply predicting the majority class, but precision reveals how many of the positive predictions are actually correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
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
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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: Precision — Precision is a core metric for binary classifiers, measuring the proportion of true positive predictions among all positive predictions. It is especially important when the cost of false positives is high, such as in spam detection or fraud alert systems.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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 30, 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.