The answer is the SMOTE transform and the Random Oversampling transform. SMOTE, or Synthetic Minority Oversampling Technique, generates synthetic samples for the minority class by interpolating between existing minority instances, creating new, realistic data points rather than simply duplicating them. Random Oversampling, on the other hand, balances the class distribution by duplicating existing minority class instances, which is a simpler but effective approach for handling imbalanced data. On the AWS Certified Machine Learning Engineer Associate MLA-C01 exam, this question tests your knowledge of SageMaker Data Wrangler’s built-in transforms for imbalanced classification, a common real-world scenario where you must choose between synthetic and duplication-based methods. A frequent trap is assuming only one technique exists, but Data Wrangler offers both for different use cases. Remember the memory tip: “SMOTE creates, Oversample repeats” to distinguish the two when handling imbalanced data.
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 scientist is using Amazon SageMaker Data Wrangler to prepare a dataset. Which TWO features of Data Wrangler can be used to handle imbalanced classification problems? (Choose two.)
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
The Random Oversampling transform to duplicate minority class instances.
Option A is correct because Amazon SageMaker Data Wrangler includes a built-in Random Oversampling transform that duplicates instances of the minority class to balance the class distribution. This directly addresses imbalanced classification by increasing the representation of the underrepresented class without generating synthetic data.
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.
✓
The Random Oversampling transform to duplicate minority class instances.
Why this is correct
Oversampling increases the minority class size.
Related concept
Read the scenario before looking for a memorised answer.
✓
The SMOTE transform to generate synthetic samples for the minority class.
Why this is correct
SMOTE creates synthetic minority samples to balance classes.
Related concept
Read the scenario before looking for a memorised answer.
✗
The Drop Duplicates transform to remove redundant rows.
Why it's wrong here
Removing duplicates does not target imbalance.
✗
Standardization to scale numerical features.
Why it's wrong here
Scaling does not affect class distribution.
✗
One-hot encoding for categorical variables.
Why it's wrong here
One-hot encoding changes feature representation but not class balance.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates may confuse data preprocessing techniques (like scaling or encoding) with class imbalance handling methods, leading them to select Standardization or one-hot encoding as solutions for imbalanced data.
Detailed technical explanation
How to think about this question
SMOTE (Synthetic Minority Oversampling Technique) works by interpolating between existing minority class instances in feature space, creating synthetic samples that are not simple duplicates. In SageMaker Data Wrangler, the SMOTE transform is implemented as a built-in step that can be applied directly within the visual workflow, allowing data scientists to balance classes before model training. A subtle behavior is that SMOTE can introduce noise if the minority class is highly scattered, so Random Oversampling may be preferred for small datasets.
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
An e-commerce site experiences heavy traffic on Black Friday and near-zero traffic during off-peak weeks. Rather than provisioning permanent large VMs, the team uses auto-scaling groups that add capacity automatically under load and reduce it overnight. Questions like this test whether you understand elasticity, availability zones, and cloud compute scaling patterns.
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.
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: The Random Oversampling transform to duplicate minority class instances. — Option A is correct because Amazon SageMaker Data Wrangler includes a built-in Random Oversampling transform that duplicates instances of the minority class to balance the class distribution. This directly addresses imbalanced classification by increasing the representation of the underrepresented class without generating synthetic data.
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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Question Discussion
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