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Data Preparation for Machine Learning →hardMultiple SelectObjective-mapped

Optimal Data Preparation Steps for XGBoost on SageMaker

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.)

A
Remove any rows with outlier values.
Why wrong: Outliers may be valid; removal should be justified, not automatic.
B
Split the data into training, validation, and test sets before any imputation.
Splitting first prevents data leakage from imputation statistics.
C
Impute missing numeric values with median or mean.
XGBoost can handle missing but imputation can improve performance if done properly.
D
For categorical features, use one-hot encoding for low cardinality and target encoding for high cardinality.
This balances dimensionality and predictive power.
E
Apply target encoding to all categorical features regardless of cardinality.
Why wrong: Target encoding for low-cardinality may lead to overfitting; one-hot is often better.

Full breakdown with real-world context →

Answer choices

Why each option matters

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.

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FAQ

Questions learners often ask

What does this MLA-C01 question test?

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.

About these practice questions

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Same concept, more angles

1 more ways this is tested on MLA-C01

These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.

Variation 1. A healthcare company is building a model to predict patient readmission rates. The dataset contains a mix of numeric features (age, blood pressure, lab test results) and categorical features (gender, diagnosis code, hospital department). The dataset has 2 million rows. The data is stored in an Amazon S3 bucket, and they use AWS Glue to catalog and preprocess the data. The data scientist notices that the 'diagnosis_code' column has 10,000 unique codes, and 20% of the rows have missing values for 'blood_pressure'. They plan to use a SageMaker built-in XGBoost model. For optimal model performance, which preprocessing steps should they apply using AWS Glue ETL?

medium

A.Impute missing 'blood_pressure' with the mean, and apply label encoding to 'diagnosis_code'.
✓ B.Impute missing 'blood_pressure' with median, and apply integer encoding to 'diagnosis_code'.
C.Replace missing 'blood_pressure' with -1 and apply one-hot encoding to 'diagnosis_code' after grouping rare codes into 'other'.
D.Apply one-hot encoding to 'diagnosis_code' and drop rows with missing 'blood_pressure'.

Why B: Option B is correct because XGBoost handles missing values natively, so median imputation for 'blood_pressure' is robust to outliers and preserves data distribution, while integer encoding (label encoding) for 'diagnosis_code' with 10,000 unique values is efficient and avoids the dimensionality explosion of one-hot encoding. AWS Glue ETL can apply these transformations using built-in functions like `Imputer` and `StringIndexer` without excessive memory overhead.

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