- A
Precision
Why wrong: Precision focuses on false positives; while important, the immediate problem is missing actual churners (false negatives). Recall is more directly relevant.
- B
Recall
Recall (sensitivity) measures the proportion of actual churners that the model correctly identifies. Improving recall ensures more churners are caught.
- C
Accuracy
Why wrong: Accuracy can be high even if the model fails to catch churners because the majority class dominates.
- D
F1 score
Why wrong: F1 balances precision and recall, but the business team's complaint is specifically about missed churners, so recall is the primary concern.
Question 123 of 1,000
AIF-C01 AI and ML Fundamentals Practice Question
This AIF-C01 practice question tests your understanding of ai and ml fundamentals. 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 training a binary classification model to predict customer churn. After training, the model achieves 95% accuracy on the test set, but the business team reports that the model almost never predicts churn correctly. Which metric should the data scientist focus on to improve the model?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue:
"never"Why it matters: Absolute qualifier. True only if the statement has zero exceptions — be cautious of options that seem obvious but break down in edge cases.
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
The model's high accuracy (95%) with poor churn prediction indicates a class imbalance where the majority class (non-churn) dominates. Recall (sensitivity) measures the proportion of actual churn cases correctly identified, which directly addresses the business need to catch churners. Improving recall will increase the true positive rate for the minority churn class.
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.
- ✗
Precision
Why it's wrong here
Precision focuses on false positives; while important, the immediate problem is missing actual churners (false negatives). Recall is more directly relevant.
- ✓
Recall
Why this is correct
Recall (sensitivity) measures the proportion of actual churners that the model correctly identifies. Improving recall ensures more churners are caught.
Clue confirmation
The clue word "never" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Accuracy
Why it's wrong here
Accuracy can be high even if the model fails to catch churners because the majority class dominates.
- ✗
F1 score
Why it's wrong here
F1 balances precision and recall, but the business team's complaint is specifically about missed churners, so recall is the primary concern.
Common exam traps
Common exam trap: answer the scenario, not the keyword
AWS often tests the misconception that high accuracy implies good model performance, especially in imbalanced classification problems, leading candidates to overlook recall as the appropriate metric for minority class detection.
Detailed technical explanation
How to think about this question
In binary classification with severe class imbalance (e.g., 5% churn, 95% non-churn), a model can achieve 95% accuracy by simply predicting the majority class for all instances, yielding zero true positives for churn. Recall is defined as TP/(TP+FN), and optimizing it forces the model to reduce false negatives, which is critical for detecting rare events like churn. Techniques such as threshold tuning, oversampling (SMOTE), or cost-sensitive learning are often used to boost recall without sacrificing precision excessively.
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 AIF-C01 question test?
AI and ML Fundamentals — This question tests AI and ML Fundamentals — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Recall — The model's high accuracy (95%) with poor churn prediction indicates a class imbalance where the majority class (non-churn) dominates. Recall (sensitivity) measures the proportion of actual churn cases correctly identified, which directly addresses the business need to catch churners. Improving recall will increase the true positive rate for the minority churn class.
What should I do if I get this AIF-C01 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Are there clue words in this question I should notice?
Yes — watch for: "never". Absolute qualifier. True only if the statement has zero exceptions — be cautious of options that seem obvious but break down in edge cases.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Last reviewed: Jul 4, 2026
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