- 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.
Quick Answer
The answer is to use SMOTE (Synthetic Minority Oversampling Technique) to generate synthetic fraudulent samples. This technique addresses severe class imbalance by creating new, plausible minority-class data points through interpolation between existing minority instances, rather than simply duplicating records, which helps the model learn robust decision boundaries without overfitting. On the AWS Certified Machine Learning Engineer Associate MLA-C01 exam, this scenario tests your understanding of how to handle imbalanced tabular data in SageMaker, often appearing as a trap where oversampling with duplication or undersampling the majority class seems tempting but leads to overfitting or information loss. A common memory tip is to think of SMOTE as “synthesizing, not copying”—it creates new data, not clones, preserving variance while balancing the classes.
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.
Option D is correct because using SMOTE generates synthetic samples for the minority class, addressing imbalance without simply duplicating data. Option A is wrong because oversampling with duplication can lead to overfitting. Option B is wrong because undersampling discards too much legitimate data, losing valuable patterns. Option C is wrong because the data is already in a tabular format, not images.
Key principle: NAT direction and interface roles matter as much as the IP address mapping. Inside/outside designation controls which traffic is translated.
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
Static NAT maps one inside address to one outside address.
- ✗
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: NAT rules depend on direction and matching traffic
NAT is not only about the public address. The inside/outside interface roles and the ACL or rule that matches traffic are just as important.
Detailed technical explanation
How to think about this question
NAT questions usually test address translation, overload/PAT behaviour, static mappings and whether the right traffic is being translated. Read the interface direction and address terms carefully.
KKey Concepts to Remember
- Static NAT maps one inside address to one outside address.
- PAT allows many inside hosts to share one public address using ports.
- Inside local and inside global describe the private and translated addresses.
- NAT ACLs identify traffic for translation, not always security filtering.
TExam Day Tips
- Identify inside and outside interfaces first.
- Check whether the scenario needs static NAT, dynamic NAT or PAT.
- Do not confuse NAT matching ACLs with normal packet-filtering intent.
Key takeaway
NAT direction and interface roles matter as much as the IP address mapping. Inside/outside designation controls which traffic is translated.
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. NAT direction and interface roles matter as much as the IP address mapping. Inside/outside designation controls which traffic is translated. 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.
Review the four NAT address types (inside local, inside global, outside local, outside global), PAT port overload, and static vs dynamic NAT use cases. Then practise related MLA-C01 NAT questions on configuration and troubleshooting.
- →
Data Preparation for Machine Learning — study guide chapter
Learn the concepts, then practise the questions
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Data Preparation for Machine Learning practice questions
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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 — Static NAT maps one inside address to one outside address..
What is the correct answer to this question?
The correct answer is: Use SMOTE (Synthetic Minority Oversampling Technique) to generate synthetic fraudulent samples. — Option D is correct because using SMOTE generates synthetic samples for the minority class, addressing imbalance without simply duplicating data. Option A is wrong because oversampling with duplication can lead to overfitting. Option B is wrong because undersampling discards too much legitimate data, losing valuable patterns. Option C is wrong because the data is already in a tabular format, not images.
What should I do if I get this MLA-C01 question wrong?
Review the four NAT address types (inside local, inside global, outside local, outside global), PAT port overload, and static vs dynamic NAT use cases. Then practise related MLA-C01 NAT questions on configuration and troubleshooting.
What is the key concept behind this question?
Static NAT maps one inside address to one outside address.
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Last reviewed: Jun 23, 2026
This MLA-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 MLA-C01 exam.
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