The answer is to increase the classification threshold for the positive class. This adjustment directly addresses the precision problem by raising the probability cutoff required to label a sample as churn, which filters out borderline false positives while still capturing most true positives, thereby preserving recall. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this scenario tests your understanding of the precision-recall trade-off after applying SMOTE, a common technique for improving precision in imbalanced binary classification. A frequent trap is assuming that oversampling alone fixes all imbalance issues, but the threshold remains a critical lever. Remember: SMOTE balances the data, but the threshold balances the cost—raise the bar to raise precision.
MLS-C01 Modeling Practice Question
This MLS-C01 practice question tests your understanding of modeling. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. 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 company is building a binary classification model to predict customer churn. The dataset is highly imbalanced (95% non-churn, 5% churn). The data scientist uses SMOTE to oversample the minority class. After training a logistic regression model, the recall for the churn class is 0.80, but the precision is only 0.10. Which action would MOST likely improve precision without significantly harming recall?
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.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Increase the classification threshold for the positive class
Option A is correct because increasing the classification threshold reduces false positives, improving precision, while still retaining many true positives. Option B is wrong because decreasing the threshold would further reduce precision. Option C is wrong because using a different oversampling technique might not directly address the threshold issue. Option D is wrong because reducing the model's complexity could reduce recall.
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.
✗
Use random oversampling instead of SMOTE
Why it's wrong here
Oversampling method may not directly improve precision; SMOTE is generally better.
✗
Reduce the number of features in the model
Why it's wrong here
Feature reduction may reduce model capacity and potentially harm recall.
✓
Increase the classification threshold for the positive class
Why this is correct
A higher threshold reduces false positives, improving precision, while likely still capturing many true 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.
✗
Decrease the classification threshold for the positive class
Why it's wrong here
Lowering the threshold increases false positives, worsening precision.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Many certification questions include familiar terms but test a specific constraint. Read the exact wording before choosing an answer that is generally true but wrong for this case.
Detailed technical explanation
How to think about this question
This question should be treated as a scenario, not a definition check. Identify the problem, the constraint and the best action. Then compare each option against those facts.
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.
Use explanations to understand the rule behind the answer.
TExam Day Tips
→Underline the problem statement mentally.
→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 MLS-C01 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
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: Increase the classification threshold for the positive class — Option A is correct because increasing the classification threshold reduces false positives, improving precision, while still retaining many true positives. Option B is wrong because decreasing the threshold would further reduce precision. Option C is wrong because using a different oversampling technique might not directly address the threshold issue. Option D is wrong because reducing the model's complexity could reduce recall.
What should I do if I get this MLS-C01 question wrong?
Identify which MLS-C01 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
Are there clue words in this question I should notice?
Yes — watch for: "most likely". Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. A data scientist wants to build a binary classifier to predict customer churn. The dataset has 10,000 records with 500 churners (5%). Which technique should the data scientist use to address class imbalance?
C.Assign higher class weights to the minority class.
D.Downsample the majority class to match the minority class size.
Why B: SMOTE generates synthetic samples for the minority class, which is appropriate for imbalanced datasets. Option A (downsampling majority class) would lose data. Option B (upweighting minority class) is possible but less common. Option D (random undersampling) also loses data.
Variation 2. A company is building a binary classification model to predict customer churn. The dataset has 10,000 samples with 500 churners (positive class). The data scientist trains a logistic regression model and obtains an accuracy of 95%. However, the model predicts all customers as non-churn. Which metric should the data scientist use to evaluate the model's performance?
easy
A.AUC-ROC
✓ B.F1-score
C.Accuracy
D.Confusion matrix
Why B: The F1-score is the harmonic mean of precision and recall, making it robust to class imbalance. Since the model predicts all customers as non-churn (accuracy 95% due to 9500 non-churners), precision for the positive class is undefined (0 true positives) and recall is 0, so the F1-score correctly reveals the model's failure to identify any churners.
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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.
Question Discussion
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