The answer is to apply random oversampling of the minority class. This technique directly addresses class imbalance during training by duplicating or synthesizing examples from the positive class, which balances the training distribution and prevents the model from becoming biased toward the 90% majority class. By mitigating skewed gradient updates, random oversampling improves recall and precision for the minority class in binary classification tasks. On the AWS Certified Machine Learning Engineer Associate MLA-C01 exam, this concept tests your understanding of data-level resampling methods versus algorithm-level adjustments like cost-sensitive learning; a common trap is choosing SMOTE when the question specifies simple duplication is sufficient, or assuming undersampling is safer despite losing valuable data. Remember the memory tip: “Oversample the underdog”—when the minority class is severely underrepresented, adding more of its examples forces the model to pay attention, directly fixing the imbalance at the data source.
MLA-C01 ML Model Development Practice Question
This MLA-C01 practice question tests your understanding of ml model development. 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 training a binary classification model using a dataset that has a severe class imbalance (90% negative, 10% positive). Which technique should be used to address the imbalance during model training?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Apply random oversampling of the minority class
Random oversampling of the minority class (Option C) directly addresses class imbalance by duplicating or synthesizing examples from the positive class, which balances the training distribution and prevents the model from becoming biased toward the majority class. This technique is specifically designed to mitigate the skewed gradient updates that occur when the minority class is underrepresented, leading to better recall and precision for the positive class in binary classification tasks.
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.
✗
Use a larger batch size
Why it's wrong here
Larger batch size does not address class imbalance; it may even exacerbate it by providing fewer minority examples per batch.
✗
Use L2 regularization
Why it's wrong here
L2 regularization prevents overfitting but does not address class imbalance.
✓
Apply random oversampling of the minority class
Why this is correct
Random oversampling balances the class distribution by replicating minority class samples.
Related concept
Read the scenario before looking for a memorised answer.
✗
Increase the learning rate
Why it's wrong here
Increasing learning rate can affect convergence but does not correct class imbalance.
Common exam traps
Common exam trap: answer the scenario, not the keyword
AWS often tests the misconception that hyperparameter tuning (like batch size or learning rate) can fix data imbalance, when in fact only data-level or algorithm-level techniques (e.g., oversampling, undersampling, or cost-sensitive learning) directly address the skewed class distribution.
Detailed technical explanation
How to think about this question
Random oversampling works by randomly replicating instances from the minority class until the class distribution is balanced, which can be implemented using libraries like imbalanced-learn in Python. However, a subtle behavior is that simple duplication can lead to overfitting on the minority class, as the model sees the same examples repeatedly; more advanced variants like SMOTE (Synthetic Minority Oversampling Technique) generate synthetic samples by interpolating between existing minority instances, which often yields better generalization. In real-world scenarios like fraud detection or medical diagnosis, where positive cases are rare, oversampling is a standard preprocessing step to ensure the model learns meaningful decision boundaries for the minority class.
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.
ML Model Development — This question tests ML Model Development — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Apply random oversampling of the minority class — Random oversampling of the minority class (Option C) directly addresses class imbalance by duplicating or synthesizing examples from the positive class, which balances the training distribution and prevents the model from becoming biased toward the majority class. This technique is specifically designed to mitigate the skewed gradient updates that occur when the minority class is underrepresented, leading to better recall and precision for the positive class in binary classification tasks.
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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