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 company is preparing a large dataset for a SageMaker built-in XGBoost model. The dataset has missing values in both numeric and categorical features, and some categorical features have high cardinality. Which THREE data preparation steps should the company take to optimize model performance? (Choose three.)
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Split the data into training, validation, and test sets before any imputation.
Option B is correct because splitting the data into training, validation, and test sets before any imputation prevents data leakage. If imputation statistics (e.g., mean, median) were computed on the full dataset, information from the validation and test sets would influence the training data, leading to overly optimistic performance estimates and poor generalization to new 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.
✗
Remove any rows with outlier values.
Why it's wrong here
Outliers may be valid; removal should be justified, not automatic.
✓
Split the data into training, validation, and test sets before any imputation.
Why this is correct
Splitting first prevents data leakage from imputation statistics.
Related concept
Read the scenario before looking for a memorised answer.
✓
Impute missing numeric values with median or mean.
Why this is correct
XGBoost can handle missing but imputation can improve performance if done properly.
Related concept
Read the scenario before looking for a memorised answer.
✓
For categorical features, use one-hot encoding for low cardinality and target encoding for high cardinality.
Why this is correct
This balances dimensionality and predictive power.
Related concept
Read the scenario before looking for a memorised answer.
✗
Apply target encoding to all categorical features regardless of cardinality.
Why it's wrong here
Target encoding for low-cardinality may lead to overfitting; one-hot is often better.
Common exam traps
Common exam trap: answer the scenario, not the keyword
AWS often tests the misconception that all data cleaning (including imputation) should be done on the full dataset before splitting, but the correct order is to split first to preserve the independence of the test set and avoid data leakage.
Detailed technical explanation
How to think about this question
Under the hood, XGBoost handles missing values natively by learning the optimal direction (left or right child) for splits when a value is missing, so imputation is not strictly required but can improve convergence speed. For high-cardinality categorical features, target encoding replaces each category with the mean of the target variable, which introduces a risk of overfitting; a common mitigation is to use a smoothed version with a prior and cross-validation folds. One-hot encoding for low-cardinality features works well because it creates sparse binary columns that XGBoost can split efficiently, but for high cardinality it would create an unwieldy number of columns, degrading performance.
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: Split the data into training, validation, and test sets before any imputation. — Option B is correct because splitting the data into training, validation, and test sets before any imputation prevents data leakage. If imputation statistics (e.g., mean, median) were computed on the full dataset, information from the validation and test sets would influence the training data, leading to overly optimistic performance estimates and poor generalization to new 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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