- A
Use class weighting or synthetic oversampling (SMOTE) during training
This addresses imbalance effectively.
- B
Randomly undersample the majority class to balance the dataset
Why wrong: Undersampling may discard valuable data and hurt performance.
- C
Collect more data until the fraud rate increases
Why wrong: More data does not change the underlying imbalance ratio.
- D
Train without any modifications; the model will naturally handle it
Why wrong: Model will likely predict majority class for everything, ignoring fraud.
Question 99 of 1,000
PMLE Solving business challenges with ML Practice Question
This PMLE practice question tests your understanding of solving business challenges with ml. 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 financial company is building a fraud detection model. The dataset has 1% fraud cases and 99% legitimate transactions. Which technique should they use to handle the class imbalance?
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
✓
Use class weighting or synthetic oversampling (SMOTE) during training
Class weights or resampling techniques like SMOTE are standard for imbalanced datasets. Option A is correct. Option B (undersampling majority) can lose information. Option C (collect more data) is impractical. Option D (no alterations) will bias the model.
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 class weighting or synthetic oversampling (SMOTE) during training
Why this is correct
This addresses imbalance effectively.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Randomly undersample the majority class to balance the dataset
Why it's wrong here
Undersampling may discard valuable data and hurt performance.
- ✗
Collect more data until the fraud rate increases
Why it's wrong here
More data does not change the underlying imbalance ratio.
- ✗
Train without any modifications; the model will naturally handle it
Why it's wrong here
Model will likely predict majority class for everything, ignoring fraud.
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 PMLE 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.
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FAQ
Questions learners often ask
What does this PMLE question test?
Solving business challenges with ML — This question tests Solving business challenges with ML — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Use class weighting or synthetic oversampling (SMOTE) during training — Class weights or resampling techniques like SMOTE are standard for imbalanced datasets. Option A is correct. Option B (undersampling majority) can lose information. Option C (collect more data) is impractical. Option D (no alterations) will bias the model.
What should I do if I get this PMLE question wrong?
Identify which PMLE 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.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 24, 2026
This PMLE practice question is part of Courseiva's free Google Cloud 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 PMLE exam.
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