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 working with a dataset containing customer demographics and purchase history. The dataset includes categorical variables with high cardinality (e.g., ZIP code, product ID). The data scientist wants to perform feature engineering to improve model performance. Which THREE feature engineering techniques should the data scientist consider? (Choose three.)
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Principal Component Analysis (PCA) to reduce dimensionality of numerical features.
Principal Component Analysis (PCA) is a dimensionality reduction technique that transforms correlated numerical features into a smaller set of uncorrelated principal components, capturing the maximum variance in the data. This is correct because the dataset includes numerical features (e.g., purchase amounts, age) where PCA can reduce noise and multicollinearity, improving model performance without losing critical information.
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.
✓
Principal Component Analysis (PCA) to reduce dimensionality of numerical features.
Why this is correct
PCA can reduce noise and multicollinearity.
Related concept
Read the scenario before looking for a memorised answer.
✗
Domain-specific feature engineering based on business rules.
Why it's wrong here
This is often useful but not a general technique applicable to all datasets.
✓
Target encoding for high-cardinality categorical variables.
Why this is correct
Target encoding replaces each category with the mean target value, useful for high cardinality.
Related concept
Read the scenario before looking for a memorised answer.
✓
Frequency encoding to represent categories by their occurrence count.
Why this is correct
Frequency encoding summarizes category prevalence.
Related concept
Read the scenario before looking for a memorised answer.
✗
One-hot encoding all categorical features.
Why it's wrong here
One-hot encoding high cardinality features creates a large sparse matrix.
Common exam traps
Common exam trap: answer the scenario, not the keyword
AWS often tests the distinction between techniques that are universally applicable (like PCA for numerical features) versus those that are specifically designed to handle high-cardinality categorical variables (like target encoding and frequency encoding), tempting candidates to choose one-hot encoding without considering its impracticality for high cardinality.
Detailed technical explanation
How to think about this question
PCA works by computing the eigenvectors and eigenvalues of the covariance matrix of the data, projecting the original features onto principal components that are orthogonal and sorted by explained variance. In practice, PCA assumes linear relationships and is sensitive to feature scaling, so standardization (e.g., Z-score normalization) must be applied beforehand. For high-cardinality categorical variables, target encoding replaces each category with the mean of the target variable, but it risks target leakage and overfitting if not regularized with smoothing or cross-validation.
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: Principal Component Analysis (PCA) to reduce dimensionality of numerical features. — Principal Component Analysis (PCA) is a dimensionality reduction technique that transforms correlated numerical features into a smaller set of uncorrelated principal components, capturing the maximum variance in the data. This is correct because the dataset includes numerical features (e.g., purchase amounts, age) where PCA can reduce noise and multicollinearity, improving model performance without losing critical information.
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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