Quick Answer
The answer is accuracy, precision, and recall. These three metrics are directly calculable from the confusion matrix because each relies solely on the four fundamental counts: true positives (TP), false positives (FP), true negatives (TN), and false negatives (FN). Accuracy is the ratio of correct predictions to total predictions, precision measures the proportion of positive identifications that were actually correct, and recall (also called sensitivity) measures the proportion of actual positives correctly identified. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this type of question tests your ability to derive performance metrics from raw confusion matrix values, often as part of model evaluation scenarios. A common trap is forgetting that recall requires FN values; here, with FN=0, recall is 100%, but the metric is still valid. A helpful memory tip: think of precision as “how many selected items are relevant” and recall as “how many relevant items are selected,” and remember that accuracy is simply the overall correctness rate.
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 binary classification model. The model's confusion matrix shows: True Positives=80, False Positives=20, True Negatives=900, False Negatives=0. Which THREE metrics can be calculated from this confusion matrix? (Choose three.)
Precision
Precision = TP/(TP+FP).
Recall
Recall = TP/(TP+FN).
AUC-ROC
Why wrong: AUC-ROC requires predicted scores, not just the confusion matrix.
Accuracy
Accuracy can be computed from TP, TN, FP, FN.
Root Mean Squared Error (RMSE)
Why wrong: RMSE is for regression tasks.
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 calculated as TP/(TP+FP) = 80/(80+20) = 0.80. This metric measures the proportion of positive identifications that were actually correct, which is directly derivable from the confusion matrix values.
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.
Precision
Why this is correct
Precision = TP/(TP+FP).
Related concept
Read the scenario before looking for a memorised answer.
Recall
Why this is correct
Recall = TP/(TP+FN).
Related concept
Read the scenario before looking for a memorised answer.
AUC-ROC
Why it's wrong here
AUC-ROC requires predicted scores, not just the confusion matrix.
Accuracy
Why this is correct
Accuracy can be computed from TP, TN, FP, FN.
Related concept
Read the scenario before looking for a memorised answer.
Root Mean Squared Error (RMSE)
Why it's wrong here
RMSE is for regression tasks.
Common exam traps
The trap here is that candidates often assume AUC-ROC can be derived from a single confusion matrix, but it actually requires the full distribution of predicted probabilities to plot the ROC curve and calculate the area under it.
Trap categories for this question
Similar concept trap
AUC-ROC requires predicted scores, not just the confusion matrix.
Detailed technical explanation
The confusion matrix provides the four fundamental counts (TP, FP, TN, FN) from which many classification metrics like precision, recall, and accuracy are directly computed. Recall (sensitivity) is TP/(TP+FN) = 80/(80+0) = 1.0, and accuracy is (TP+TN)/(TP+TN+FP+FN) = (80+900)/(80+900+20+0) = 0.98. These metrics are threshold-dependent and summarize performance at a single operating point, unlike AUC-ROC which evaluates performance across all thresholds.
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
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FAQ
Modeling — This question tests Modeling — Read the scenario before looking for a memorised answer..
The correct answer is: Precision — Precision is calculated as TP/(TP+FP) = 80/(80+20) = 0.80. This metric measures the proportion of positive identifications that were actually correct, which is directly derivable from the confusion matrix values.
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.
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Last reviewed: Jun 24, 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.
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