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Modeling →easyMultiple SelectObjective-mapped

Confusion Matrix Metrics: Precision and Recall for AWS ML Specialty

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 evaluating a classification model. The confusion matrix shows that the model has 50 true positives, 100 true negatives, 20 false positives, and 30 false negatives. Which TWO metrics can be calculated from this confusion matrix? (Choose two.)

A
R-squared
Why wrong: R-squared is for regression, not classification.
B
F1 score
Why wrong: F1 requires both precision and recall, but it is not directly from the confusion matrix without calculation. However, it is derivable. But since the question asks for 'TWO', precision and recall are more fundamental.
C
Recall
Recall = TP/(TP+FN) can be directly calculated.
D
Root mean squared error
Why wrong: RMSE is for regression.
E
Precision
Precision = TP/(TP+FP) can be directly calculated.

Full breakdown with real-world context →

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 (also known as sensitivity) is calculated as TP / (TP + FN) = 50 / (50 + 30) = 0.625, measuring the proportion of actual positives correctly identified. Precision is calculated as TP / (TP + FP) = 50 / (50 + 20) = 0.714, measuring the proportion of positive predictions that are correct. Both metrics are directly derived from the four values in the confusion matrix.

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.

✗
R-squared
Why it's wrong here
R-squared is for regression, not classification.
✗
F1 score
Why it's wrong here
F1 requires both precision and recall, but it is not directly from the confusion matrix without calculation. However, it is derivable. But since the question asks for 'TWO', precision and recall are more fundamental.
✓
Recall
Why this is correct
Recall = TP/(TP+FN) can be directly calculated.
Related concept
Read the scenario before looking for a memorised answer.
✗
Root mean squared error
Why it's wrong here
RMSE is for regression.
✓
Precision
Why this is correct
Precision = TP/(TP+FP) can be directly calculated.
Related concept
Read the scenario before looking for a memorised answer.

Common exam traps

Common exam trap: answer the scenario, not the keyword

The MLS-C01 exam often tests the distinction between metrics that are directly computed from the confusion matrix (like precision and recall) versus metrics that require additional calculations or are specific to regression tasks, leading candidates to mistakenly select F1 score as a direct metric or R-squared as applicable to classification.

Trap categories for this question

Similar concept trap
F1 requires both precision and recall, but it is not directly from the confusion matrix without calculation. However, it is derivable. But since the question asks for 'TWO', precision and recall are more fundamental.

Detailed technical explanation

How to think about this question

The confusion matrix provides the raw counts for true positives, true negatives, false positives, and false negatives, which are the foundation for computing classification metrics like accuracy, precision, recall, specificity, and F1 score. In practice, when dealing with imbalanced datasets, precision and recall are often preferred over accuracy because they reveal performance on the minority class, and the F1 score balances both. The confusion matrix is also used to derive the ROC curve by varying the classification threshold, but the matrix itself only gives a single snapshot at a fixed threshold.

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.

Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.

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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 (also known as sensitivity) is calculated as TP / (TP + FN) = 50 / (50 + 30) = 0.625, measuring the proportion of actual positives correctly identified. Precision is calculated as TP / (TP + FP) = 50 / (50 + 20) = 0.714, measuring the proportion of positive predictions that are correct. Both metrics are directly derived from the four values in the confusion matrix.

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.

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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.