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.
Refer to the exhibit.
Confusion Matrix:
Predicted Positive Predicted Negative
Actual Positive 80 20
Actual Negative 10 90Refer to the exhibit. What is the recall of the model?
Refer to the exhibit.
Confusion Matrix:
Predicted Positive Predicted Negative
Actual Positive 80 20
Actual Negative 10 900.85
Why wrong: That is accuracy.
0.80
Recall = 80/(80+20) = 0.80.
0.89
Why wrong: That is precision.
0.90
Why wrong: That is specificity.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
0.80
Recall is calculated as True Positives divided by the sum of True Positives and False Negatives. From the confusion matrix, True Positives = 80 and False Negatives = 20, so recall = 80 / (80 + 20) = 0.80. Option B is correct.
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.
0.85
Why it's wrong here
That is accuracy.
0.80
Why this is correct
Recall = 80/(80+20) = 0.80.
Related concept
Read the scenario before looking for a memorised answer.
0.89
Why it's wrong here
That is precision.
0.90
Why it's wrong here
That is specificity.
Common exam traps
Cisco often tests the distinction between recall and precision, where candidates mistakenly compute precision (TP/(TP+FP)) instead of recall, leading to option A (0.85).
Detailed technical explanation
Recall, also known as sensitivity or true positive rate, measures the proportion of actual positives correctly identified. In imbalanced datasets (e.g., fraud detection), recall is critical because missing a positive (false negative) can be costly. The confusion matrix provides the raw counts: TP=80, FN=20, FP=14, TN=36, and recall = TP/(TP+FN) = 80/100 = 0.80.
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: 0.80 — Recall is calculated as True Positives divided by the sum of True Positives and False Negatives. From the confusion matrix, True Positives = 80 and False Negatives = 20, so recall = 80 / (80 + 20) = 0.80. Option B is correct.
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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