Quick Answer
The correct answer is that the model has many false positives. This follows directly from the precision-recall tradeoff: high recall means the model captures nearly all actual positives, minimizing false negatives, but low precision indicates that a large proportion of its positive predictions are incorrect. Since precision equals true positives divided by all predicted positives, a low value with high recall forces the number of false positives to be high relative to true positives. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this concept tests your understanding of how classification metrics interact, often appearing in scenario-based questions where you must infer model behavior from metric values. A common trap is confusing high recall with overall accuracy; remember that high recall alone does not guarantee good performance. For a memory tip, think of a “generous” model that flags almost everything as positive—it catches all the real positives (high recall) but also floods you with false alarms (low precision).
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 machine learning engineer is evaluating a binary classification model. The model has a high recall but low precision. Which of the following is the most likely consequence?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue: "most likely"
Why it matters: Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
The model has many false positives.
Low precision means a high rate of false positives.
The model has few false negatives.
Why wrong: High recall implies few false negatives, but that is not a consequence of low precision.
The model misses many positive cases.
Why wrong: High recall means few misses.
The model has few false positives.
Why wrong: Low precision indicates many false positives.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
The model has many false positives.
High recall means the model correctly identifies most positive cases (few false negatives), but low precision indicates that among the cases predicted as positive, many are actually negative. This directly implies a high number of false positives, as precision = TP/(TP+FP) and a low precision with high recall forces FP to be large relative to TP.
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.
The model has many false positives.
Why this is correct
Low precision means a high rate of false positives.
Clue confirmation
The clue word "most likely" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
The model has few false negatives.
Why it's wrong here
High recall implies few false negatives, but that is not a consequence of low precision.
The model misses many positive cases.
Why it's wrong here
High recall means few misses.
The model has few false positives.
Why it's wrong here
Low precision indicates many false positives.
Common exam traps
Cisco often tests the precision-recall trade-off by asking candidates to confuse the definitions of false positives and false negatives, leading them to incorrectly associate high recall with many false positives instead of few false negatives.
Detailed technical explanation
Precision and recall trade off through the decision threshold. Lowering the threshold increases recall (catches more true positives) but also increases false positives, reducing precision. In imbalanced datasets, a model with high recall but low precision may be overly sensitive, flagging many negatives as positive, which is common in fraud detection where missing a fraud case is costly but false alarms are tolerated.
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 exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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FAQ
Modeling — This question tests Modeling — Read the scenario before looking for a memorised answer..
The correct answer is: The model has many false positives. — High recall means the model correctly identifies most positive cases (few false negatives), but low precision indicates that among the cases predicted as positive, many are actually negative. This directly implies a high number of false positives, as precision = TP/(TP+FP) and a low precision with high recall forces FP to be large relative to TP.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Yes — watch for: "most likely". Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
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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