The answer is to apply SMOTE to generate synthetic fraud samples because this technique directly addresses the extreme class imbalance—only 0.1% fraud in a million transactions—by creating new, interpolated minority instances rather than simply duplicating existing ones. SMOTE allows the random forest model to learn more robust decision boundaries for the fraud class, improving recall without the noise of random oversampling or the information loss of undersampling. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this scenario tests your understanding of how to handle imbalanced datasets in SageMaker, often appearing as a trap where high accuracy masks poor recall. A common mistake is choosing random oversampling or undersampling, but SMOTE is preferred because it generates realistic synthetic samples that preserve feature relationships. Remember the mnemonic: SMOTE Saves Minority Over-sampled Training Examples—it synthesizes, not duplicates.
MLS-C01 Modeling Practice Question
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 company uses Amazon SageMaker to train a model for fraud detection. The dataset has 1 million transactions, with 0.1% fraud. The data scientist trains a random forest model and achieves 99.9% accuracy but 0% recall on the fraud class. Which technique is most likely to improve recall without significantly reducing precision?
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
✓
Apply SMOTE to generate synthetic fraud samples
Option C is correct because SMOTE (Synthetic Minority Oversampling Technique) generates synthetic fraud samples by interpolating between existing minority class instances, which directly addresses the extreme class imbalance (0.1% fraud). This increases the representation of the fraud class in the training data, allowing the random forest model to learn decision boundaries that capture fraud patterns, thereby improving recall without introducing the noise or information loss associated with other methods.
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.
✗
Tune the classification threshold
Why it's wrong here
Threshold tuning can improve recall but often at the cost of precision; it doesn't directly address data imbalance.
✗
Use cost-sensitive learning with a high cost for fraud misclassification
Why it's wrong here
Cost-sensitive learning can improve recall but may significantly reduce precision if the cost is too high.
✓
Apply SMOTE to generate synthetic fraud samples
Why this is correct
SMOTE creates synthetic instances of the minority class, balancing the dataset and improving recall while maintaining precision.
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.
✗
Undersample the majority class
Why it's wrong here
Undersampling reduces the dataset size significantly, potentially losing valuable information and harming precision.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often assume cost-sensitive learning (Option B) is the best approach for imbalanced data, but in extreme imbalance with 0% recall, oversampling techniques like SMOTE are more effective because they directly increase the minority class representation rather than just adjusting penalties.
Detailed technical explanation
How to think about this question
SMOTE works by selecting a minority class sample, finding its k-nearest neighbors (typically k=5), and creating synthetic samples along the line segments connecting the sample to its neighbors. This interpolation avoids simple duplication and helps the model generalize better. In a real-world fraud detection scenario with 1 million transactions and only 1,000 fraud cases, SMOTE can generate thousands of synthetic fraud samples, enabling the random forest to learn more robust splits without overfitting to noise.
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 startup's cloud architect reviews their monthly bill and notices costs are higher than expected for a long-running batch job. Switching from on-demand instances to Reserved Instances — or using Spot/Preemptible VMs — can reduce compute costs by up to 72 %. Questions like this test whether you understand the tradeoffs between commitment, flexibility, and cost across cloud pricing models.
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.
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: Apply SMOTE to generate synthetic fraud samples — Option C is correct because SMOTE (Synthetic Minority Oversampling Technique) generates synthetic fraud samples by interpolating between existing minority class instances, which directly addresses the extreme class imbalance (0.1% fraud). This increases the representation of the fraud class in the training data, allowing the random forest model to learn decision boundaries that capture fraud patterns, thereby improving recall without introducing the noise or information loss associated with other methods.
What should I do if I get this MLS-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: "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 company uses Amazon SageMaker to train a model for detecting fraudulent transactions. The dataset is highly imbalanced (99.9% legitimate, 0.1% fraudulent). Which approach is most effective to address this imbalance?
medium
A.Use class weights in the loss function
✓ B.Apply SMOTE to generate synthetic samples
C.Random oversampling of the minority class
D.Collect more data for the minority class
Why B: Option C is correct because SMOTE generates synthetic samples for the minority class, effectively balancing the dataset. Option A is wrong because random oversampling may cause overfitting. Option B is wrong because using class weights directly in the loss function is also valid but SMOTE is often more effective. Option D is wrong because collecting more data is good but may not be feasible.
Variation 2. A company uses Amazon SageMaker to train a model for fraud detection. The training data is highly imbalanced. The data scientist uses SMOTE to oversample the minority class. However, the model still has poor recall on the minority class. Which additional technique should the data scientist consider?
hard
A.One-vs-rest encoding
✓ B.Use class weights in the loss function
C.L1 regularization
D.Principal component analysis (PCA)
Why B: Cost-sensitive learning assigns higher penalty to misclassifications of minority class, addressing imbalance. Option A is for feature selection, B is regularization, D is for multi-class.
Variation 3. A company uses Amazon SageMaker to train a model for fraud detection. The dataset has 1 million samples with 200 features. The data is highly imbalanced (0.1% fraud). The team wants to use a random forest model. Which technique should they use to handle the class imbalance during training?
B.Use class weights inversely proportional to class frequencies
C.Random undersampling of the majority class
D.Adjust the decision threshold after training
Why A: SMOTE generates synthetic samples of the minority class to balance the dataset. Option A is wrong because undersampling discards majority class data, losing information. Option B is wrong because class weights adjust loss function but are not specific to random forest. Option D is wrong because threshold tuning is post-training, not during training.
Share a tip, memory trick, or ask about the reasoning behind this question. Do not post real exam questions, leaked content, braindumps, or copyrighted exam material. Comments are moderated and may be removed without notice.
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
Share a tip, memory trick, or ask about the reasoning behind this question. Do not post real exam questions, leaked content, braindumps, or copyrighted exam material. Comments are moderated and may be removed without notice.
Sign in to join the discussion.