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Data Preparation for Machine Learning →hardMultiple ChoiceObjective-mapped

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 training a binary classifier on a highly imbalanced dataset (1:100 class ratio). The dataset contains 500,000 rows and 30 features. The data is stored in S3 in Parquet format. The data scientist wants to use SageMaker's built-in XGBoost algorithm. Which data preparation technique should the data scientist apply to best address the class imbalance without causing data leakage?

Clue words in this question

Noticing these words before you look at the options changes how you read each choice.

Clue: "best"
Why it matters: Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.

Question 1hardmultiple choice

Full question →

A
Undersample the majority class to create a balanced dataset, then split.
Why wrong: Undersampling may lose valuable information and still requires careful splitting.
B
Use the scale_pos_weight parameter in XGBoost to assign higher weight to the minority class.
This is the correct approach; it adjusts class weights without modifying the dataset.
C
Oversample the minority class using SMOTE on the entire dataset before splitting into train/validation sets.
Why wrong: SMOTE before split causes data leakage; the validation set may contain synthetic examples derived from training data.
D
Randomly oversample the minority class by duplicating rows, then perform stratified train/test split.
Why wrong: Duplication before split can leak the same rows into both training and validation.

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

✓

Use the scale_pos_weight parameter in XGBoost to assign higher weight to the minority class.

The scale_pos_weight parameter in XGBoost directly adjusts the loss function to penalize misclassifications of the minority class more heavily, effectively handling class imbalance without modifying the dataset. This avoids data leakage because the weighting is applied during training only, not during preprocessing, and does not involve any synthetic data generation or resampling that could inadvertently expose test 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.

✗
Undersample the majority class to create a balanced dataset, then split.
Why it's wrong here
Undersampling may lose valuable information and still requires careful splitting.
✓
Use the scale_pos_weight parameter in XGBoost to assign higher weight to the minority class.
Why this is correct
This is the correct approach; it adjusts class weights without modifying the dataset.
Clue confirmation
The clue word "best" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
✗
Oversample the minority class using SMOTE on the entire dataset before splitting into train/validation sets.
Why it's wrong here
SMOTE before split causes data leakage; the validation set may contain synthetic examples derived from training data.
✗
Randomly oversample the minority class by duplicating rows, then perform stratified train/test split.
Why it's wrong here
Duplication before split can leak the same rows into both training and validation.

Common exam traps

Common exam trap: answer the scenario, not the keyword

AWS often tests the misconception that resampling techniques (like SMOTE or random oversampling) are always safe, when in fact applying them before splitting introduces data leakage, whereas built-in parameters like scale_pos_weight avoid this pitfall.

Detailed technical explanation

How to think about this question

XGBoost's scale_pos_weight works by scaling the gradient of the loss function for minority class samples, effectively increasing the weight of their contribution to the objective. The typical formula is scale_pos_weight = sum(negative_instances) / sum(positive_instances), which for a 1:100 ratio would be 100. This parameter is applied during training only, so it does not alter the dataset or cause leakage, and it is computationally efficient compared to resampling techniques that increase data volume.

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 media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.

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.

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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: Use the scale_pos_weight parameter in XGBoost to assign higher weight to the minority class. — The scale_pos_weight parameter in XGBoost directly adjusts the loss function to penalize misclassifications of the minority class more heavily, effectively handling class imbalance without modifying the dataset. This avoids data leakage because the weighting is applied during training only, not during preprocessing, and does not involve any synthetic data generation or resampling that could inadvertently expose test 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.

Are there clue words in this question I should notice?

Yes — watch for: "best". Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.

What is the key concept behind this question?

Read the scenario before looking for a memorised answer.

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This MLA-C01 practice question is part of Courseiva's free Amazon Web Services certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the MLA-C01 exam.