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 performing feature engineering for a dataset with both numerical and categorical features. The data scientist wants to apply transformations that preserve the interpretability of the features. Which TWO transformations should the data scientist use? (Select TWO)
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Log transformation of skewed numerical features
Log transformation is correct because it reduces skewness in numerical features by compressing the scale of large values, making the distribution more normal while preserving the original feature's interpretability (e.g., a log-transformed income value still relates to income). This is a monotonic transformation, so the order of values is maintained, and the feature remains directly understandable.
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.
✓
Log transformation of skewed numerical features
Why this is correct
Log transformation reduces skewness while keeping feature order.
Related concept
Read the scenario before looking for a memorised answer.
✗
Target encoding of high-cardinality categorical features
Why it's wrong here
Target encoding can cause data leakage and reduces interpretability.
✓
Standard scaling of numerical features
Why this is correct
Standard scaling is reversible and preserves feature relationships.
Related concept
Read the scenario before looking for a memorised answer.
✗
PCA dimensionality reduction
Why it's wrong here
PCA transforms features into uninterpretable components.
✗
One-hot encoding of categorical features
Why it's wrong here
One-hot encoding adds many columns, complicating interpretability.
Common exam traps
Common exam trap: answer the scenario, not the keyword
AWS often tests the misconception that one-hot encoding always preserves interpretability (it does, but the question pairs it with target encoding as a distractor), leading candidates to select one-hot encoding instead of recognizing that standard scaling is the correct second choice for numerical features.
Detailed technical explanation
How to think about this question
Log transformation is typically applied using natural log (ln) or log base 10, and it works best on positive, right-skewed data (e.g., revenue, population). Standard scaling (Z-score normalization) subtracts the mean and divides by the standard deviation, producing features with mean 0 and variance 1; this preserves the relative distances between data points and the feature's shape, making it interpretable as standard deviations from the mean. In real-world scenarios, combining log transformation on skewed features and standard scaling on others is common in linear models like logistic regression or SVM, where feature scales affect convergence and coefficient interpretation.
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: Log transformation of skewed numerical features — Log transformation is correct because it reduces skewness in numerical features by compressing the scale of large values, making the distribution more normal while preserving the original feature's interpretability (e.g., a log-transformed income value still relates to income). This is a monotonic transformation, so the order of values is maintained, and the feature remains directly understandable.
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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