- A
Undersample the majority class until the dataset is balanced
Why wrong: Undersampling discards a large amount of data, which can lead to loss of valuable information and reduced model performance.
- B
Set the `scale_pos_weight` hyperparameter to `sum(negative cases) / sum(positive cases)`
This is the standard way to handle imbalance in XGBoost; it boosts the weight of the minority class during training.
- C
Use the `max_delta_step` hyperparameter to increase the learning rate for the majority class
Why wrong: `max_delta_step` helps with convergence but does not directly address class imbalance; learning rate is uniform across classes.
- D
Use SMOTE to oversample the minority class before passing the data to XGBoost
Why wrong: SMOTE can be used, but SageMaker's XGBoost has built-in class weighting which is simpler and avoids generating synthetic data that may not reflect the true distribution.
MLA-C01 Practice Question: A data scientist is building a binary…
This MLA-C01 practice question tests your understanding of a data scientist is building a binary…. 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 building a binary classification model on a highly imbalanced dataset where the positive class represents only 1% of the data. The scientist needs to train the model using Amazon SageMaker's built-in XGBoost algorithm. Which strategy should be used to address the class imbalance?
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
Set the `scale_pos_weight` hyperparameter to `sum(negative cases) / sum(positive cases)`
Option B is correct because XGBoost's `scale_pos_weight` hyperparameter is specifically designed to handle class imbalance by adjusting the weight of the positive class during training. Setting it to `sum(negative cases) / sum(positive cases)` (i.e., 99/1 = 99) tells the algorithm to penalize misclassifications of the minority class more heavily, effectively balancing the gradient updates. This is the recommended approach for built-in XGBoost in SageMaker, as it directly modifies the loss function without altering the dataset.
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 until the dataset is balanced
Why it's wrong here
Undersampling discards a large amount of data, which can lead to loss of valuable information and reduced model performance.
- ✓
Set the `scale_pos_weight` hyperparameter to `sum(negative cases) / sum(positive cases)`
Why this is correct
This is the standard way to handle imbalance in XGBoost; it boosts the weight of the minority class during training.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Use the `max_delta_step` hyperparameter to increase the learning rate for the majority class
Why it's wrong here
`max_delta_step` helps with convergence but does not directly address class imbalance; learning rate is uniform across classes.
- ✗
Use SMOTE to oversample the minority class before passing the data to XGBoost
Why it's wrong here
SMOTE can be used, but SageMaker's XGBoost has built-in class weighting which is simpler and avoids generating synthetic data that may not reflect the true distribution.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse `scale_pos_weight` with resampling techniques (like SMOTE or undersampling) or with other hyperparameters like `max_delta_step`, assuming any imbalance-handling method is equally valid, but the exam expects knowledge of the specific built-in mechanism for SageMaker's XGBoost.
Detailed technical explanation
How to think about this question
Under the hood, `scale_pos_weight` works by scaling the gradient and hessian for positive class samples during training, effectively making the algorithm focus more on minority class errors. This is equivalent to adjusting the loss function's weight parameter, which is more computationally efficient than resampling. In real-world scenarios like fraud detection (where positive cases are <1%), using `scale_pos_weight` avoids the overhead of generating synthetic data and preserves the original data distribution, which is critical for maintaining model calibration.
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.
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FAQ
Questions learners often ask
What does this MLA-C01 question test?
Read the scenario before looking for a memorised answer.
What is the correct answer to this question?
The correct answer is: Set the `scale_pos_weight` hyperparameter to `sum(negative cases) / sum(positive cases)` — Option B is correct because XGBoost's `scale_pos_weight` hyperparameter is specifically designed to handle class imbalance by adjusting the weight of the positive class during training. Setting it to `sum(negative cases) / sum(positive cases)` (i.e., 99/1 = 99) tells the algorithm to penalize misclassifications of the minority class more heavily, effectively balancing the gradient updates. This is the recommended approach for built-in XGBoost in SageMaker, as it directly modifies the loss function without altering the dataset.
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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Last reviewed: Jul 4, 2026
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.
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