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 financial services company is building a fraud detection model using Amazon SageMaker. The dataset has 10 million transactions, with 0.1% fraudulent. They train an XGBoost model with default hyperparameters. The model achieves 99.9% accuracy on the test set, but only catches 10% of actual fraud cases. The company wants to maximize the number of fraud cases caught while keeping the false positive rate below 5%. The data scientist has already tried adjusting the class weights and threshold, but the recall is still low. What should the data scientist do next?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Use a different algorithm such as a balanced random forest or SMOTE with XGBoost
Option C is correct because the model is underfitting the minority class; XGBoost with default settings may not handle extreme imbalance well. Using a specialized algorithm like balanced random forest or SMOTE can improve recall. Option A (more data) may not help if the new data is also imbalanced. Option B (PCA) reduces dimensionality but not imbalance. Option D (larger instance) does not improve model performance.
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.
✗
Collect more data, especially fraudulent transactions, to balance the dataset
Why it's wrong here
Collecting more fraud data may help, but it's time-consuming and may not be feasible.
✓
Use a different algorithm such as a balanced random forest or SMOTE with XGBoost
Why this is correct
Balanced random forest or SMOTE are designed to handle imbalanced datasets.
Related concept
Read the scenario before looking for a memorised answer.
✗
Apply PCA to reduce the number of features and prevent overfitting
Why it's wrong here
PCA does not address class imbalance.
✗
Use a larger instance type to train for more epochs
Why it's wrong here
More epochs won't solve underfitting of minority class.
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: Use a different algorithm such as a balanced random forest or SMOTE with XGBoost — Option C is correct because the model is underfitting the minority class; XGBoost with default settings may not handle extreme imbalance well. Using a specialized algorithm like balanced random forest or SMOTE can improve recall. Option A (more data) may not help if the new data is also imbalanced. Option B (PCA) reduces dimensionality but not imbalance. Option D (larger instance) does not improve model performance.
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.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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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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