- A
Apply data augmentation using image transformations because fraud detection is like image classification.
Why wrong: This is a tabular dataset; image augmentation is not applicable.
- B
Randomly oversample the fraudulent class to match the legitimate count by duplicating existing fraud records.
Why wrong: Simple duplication risks overfitting and does not add new information.
- C
Use SMOTE (Synthetic Minority Oversampling Technique) to generate synthetic fraudulent samples.
SMOTE creates synthetic examples by interpolating between existing minority instances, reducing overfitting risk.
- D
Randomly undersample the legitimate class to 1,000 samples to create a balanced dataset.
Why wrong: Undersampling discards 999,000 legitimate samples, losing significant information.
MLA-C01 Data Preparation for Machine Learning Practice Question
This MLA-C01 practice question tests your understanding of data preparation for machine learning. 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 science team is building a model to predict fraudulent transactions. The dataset has 1 million legitimate transactions and only 1,000 fraudulent ones. They plan to use Amazon SageMaker to train a model. Which data preparation technique should they apply to address the severe class imbalance before training?
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 SMOTE (Synthetic Minority Oversampling Technique) to generate synthetic fraudulent samples.
SMOTE (Synthetic Minority Oversampling Technique) is the correct choice because it generates synthetic fraudulent samples by interpolating between existing minority class instances in feature space, rather than simply duplicating records. This creates more diverse and realistic training data, reducing overfitting risk while addressing the severe 1:1000 class imbalance. Amazon SageMaker's built-in algorithms and data processing capabilities can easily integrate SMOTE-applied datasets for training.
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.
- ✗
Apply data augmentation using image transformations because fraud detection is like image classification.
Why it's wrong here
This is a tabular dataset; image augmentation is not applicable.
- ✗
Randomly oversample the fraudulent class to match the legitimate count by duplicating existing fraud records.
Why it's wrong here
Simple duplication risks overfitting and does not add new information.
- ✓
Use SMOTE (Synthetic Minority Oversampling Technique) to generate synthetic fraudulent samples.
Why this is correct
SMOTE creates synthetic examples by interpolating between existing minority instances, reducing overfitting risk.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Randomly undersample the legitimate class to 1,000 samples to create a balanced dataset.
Why it's wrong here
Undersampling discards 999,000 legitimate samples, losing significant information.
Common exam traps
Common exam trap: answer the scenario, not the keyword
AWS often tests the misconception that simple random oversampling (Option B) is sufficient, but the trap is that it causes overfitting, whereas SMOTE's synthetic generation provides better generalization for imbalanced datasets.
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 in feature space. This preserves the underlying data distribution while increasing minority class representation, and is particularly effective for tabular data where feature correlations matter. In real-world fraud detection, SMOTE helps models learn subtle fraud patterns without the bias introduced by simple duplication or the data loss from undersampling.
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 MLA-C01 question test?
Data Preparation for Machine Learning — This question tests Data Preparation for Machine Learning — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Use SMOTE (Synthetic Minority Oversampling Technique) to generate synthetic fraudulent samples. — SMOTE (Synthetic Minority Oversampling Technique) is the correct choice because it generates synthetic fraudulent samples by interpolating between existing minority class instances in feature space, rather than simply duplicating records. This creates more diverse and realistic training data, reducing overfitting risk while addressing the severe 1:1000 class imbalance. Amazon SageMaker's built-in algorithms and data processing capabilities can easily integrate SMOTE-applied datasets for training.
What should I do if I get this MLA-C01 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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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