The answer is feature selection using correlation analysis and data augmentation. Feature selection using correlation analysis reduces overfitting by identifying and removing redundant or highly correlated features, which simplifies the model and prevents it from learning noise from irrelevant predictors. Data augmentation artificially increases the diversity of the training set by adding noise or transformations, helping the model generalize better to unseen data. On the AWS Certified Machine Learning Engineer Associate MLA-C01 exam, this question tests your understanding of data preparation techniques that directly combat overfitting before model training begins—a common trap is confusing augmentation with simply adding more raw data, when in fact it creates synthetic variations. A useful memory tip: think of “cut and grow”—correlation analysis cuts away redundant features, while data augmentation grows the dataset’s variety, both reducing the risk of overfitting.
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 machine learning engineer is preparing a dataset for a multiclass classification task. The dataset has 10 features and 100,000 rows. Which TWO techniques should the engineer use to reduce the risk of overfitting during data preparation?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Data augmentation (e.g., adding noise)
Data augmentation (A) is correct because it artificially increases the diversity of the training set by adding noise or transformations, which helps the model generalize better and reduces overfitting. Feature selection using correlation analysis (E) is correct because it removes redundant or highly correlated features, simplifying the model and minimizing the risk of learning noise from irrelevant predictors.
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.
✓
Data augmentation (e.g., adding noise)
Why this is correct
Increases training data diversity, reducing overfitting.
Related concept
Read the scenario before looking for a memorised answer.
✗
SMOTE to balance classes
Why it's wrong here
Addresses class imbalance, not general overfitting.
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
AWS often tests the distinction between techniques that address overfitting versus those that handle other data issues like imbalance or skewness, leading candidates to confuse SMOTE or log transforms as overfitting remedies.
Detailed technical explanation
How to think about this question
Data augmentation works by creating modified copies of existing data (e.g., adding Gaussian noise with mean 0 and small variance), which acts as a form of regularization by preventing the model from memorizing exact samples. Feature selection via correlation analysis typically uses a threshold (e.g., Pearson correlation coefficient > 0.9) to identify and drop redundant features, reducing the model's capacity and variance. In practice, combining these techniques is common in high-dimensional datasets to combat overfitting before model training begins.
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.
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: Data augmentation (e.g., adding noise) — Data augmentation (A) is correct because it artificially increases the diversity of the training set by adding noise or transformations, which helps the model generalize better and reduces overfitting. Feature selection using correlation analysis (E) is correct because it removes redundant or highly correlated features, simplifying the model and minimizing the risk of learning noise from irrelevant predictors.
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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