Quick Answer
The answer is to set `scale_pos_weight=19` and `subsample=0.8`. This is correct because in a dataset with only 5% churn, the ratio of negative to positive samples is 95:5, or 19:1; the `scale_pos_weight` hyperparameter directly applies this ratio to penalize misclassifications of the minority class more heavily, effectively rebalancing the gradient updates. The `subsample=0.8` introduces row-level stochasticity, which is critical for preventing overfitting when the minority class is so small. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this tests your understanding of how XGBoost handles imbalanced classification without separate resampling—a common trap is to set `scale_pos_weight` to the minority class percentage (e.g., 0.05) instead of the class ratio. A reliable memory tip: think of the weight as “how many negatives for every one positive”—so 19 negatives per 1 positive means weight equals 19.
This MLS-C01 practice question tests your understanding of modeling. Compare every option against the stated constraints before choosing — the best answer satisfies all requirements, not just the most obvious one. 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 machine learning engineer is building a binary classification model to predict customer churn. The dataset is highly imbalanced (5% churn). The engineer wants to use Amazon SageMaker's built-in XGBoost algorithm. Which combination of hyperparameters is most appropriate for this scenario?
scale_pos_weight=19, subsample=0.8
Correct ratio and subsample for regularization.
scale_pos_weight=0.05, subsample=0.8
Why wrong: scale_pos_weight should be >1 for minority class, not <1.
scale_pos_weight=19, subsample=1.0
Why wrong: subsample=1.0 may cause overfitting.
scale_pos_weight=1, subsample=1.0
Why wrong: scale_pos_weight=1 does not handle imbalance; subsample=1.0 may overfit.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
scale_pos_weight=19, subsample=0.8
In a highly imbalanced dataset with only 5% churn, the ratio of negative to positive classes is 95:5, or 19:1. The `scale_pos_weight` hyperparameter in XGBoost should be set to this ratio (19) to penalize misclassifications of the minority class more heavily. A `subsample` of 0.8 introduces stochasticity and helps prevent overfitting, which is especially important when the minority class is small.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
scale_pos_weight=19, subsample=0.8
Why this is correct
Correct ratio and subsample for regularization.
Related concept
Read the scenario before looking for a memorised answer.
scale_pos_weight=0.05, subsample=0.8
Why it's wrong here
scale_pos_weight should be >1 for minority class, not <1.
scale_pos_weight=19, subsample=1.0
Why it's wrong here
subsample=1.0 may cause overfitting.
scale_pos_weight=1, subsample=1.0
Why it's wrong here
scale_pos_weight=1 does not handle imbalance; subsample=1.0 may overfit.
Common exam traps
The trap here is that candidates often confuse `scale_pos_weight` with a simple class weight or mistakenly think a value less than 1 is needed for the minority class, when in fact it should be the ratio of majority to minority class counts.
Detailed technical explanation
XGBoost's `scale_pos_weight` internally adjusts the gradient and hessian calculations for the positive class, effectively multiplying the loss contribution of minority class samples by the specified weight. This is equivalent to using a weighted loss function but is implemented more efficiently at the tree-splitting level. In practice, setting `scale_pos_weight` to the inverse class ratio (sum(negative)/sum(positive)) is a standard heuristic, but fine-tuning via hyperparameter optimization can yield better results depending on the cost of false negatives versus false positives.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
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
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Modeling — study guide chapter
Learn the concepts, then practise the questions
Modeling practice questions
Targeted practice on this topic area only
All MLS-C01 questions
1,755 questions across all exam domains
AWS Certified Machine Learning Specialty MLS-C01 study guide
Full concept coverage aligned to exam objectives
MLS-C01 practice test guide
How to use practice tests most effectively before exam day
Related practice questions
Use these pages to review the topic behind this question. This is how one missed question becomes focused revision.
Practise MLS-C01 questions linked to Data Engineering.
Practise MLS-C01 questions linked to Machine Learning Implementation and Operations.
Practise MLS-C01 questions linked to Modeling.
Practise MLS-C01 questions linked to Exploratory Data Analysis.
Practise MLS-C01 questions linked to MLS-C01 fundamentals.
Practise MLS-C01 questions linked to MLS-C01 scenario.
Practise MLS-C01 questions linked to MLS-C01 troubleshooting.
Practice this exam
Short sessions build daily habit. Longer sessions build exam-day stamina. Try a timed session to simulate real conditions.
FAQ
Modeling — This question tests Modeling — Read the scenario before looking for a memorised answer..
The correct answer is: scale_pos_weight=19, subsample=0.8 — In a highly imbalanced dataset with only 5% churn, the ratio of negative to positive classes is 95:5, or 19:1. The `scale_pos_weight` hyperparameter in XGBoost should be set to this ratio (19) to penalize misclassifications of the minority class more heavily. A `subsample` of 0.8 introduces stochasticity and helps prevent overfitting, which is especially important when the minority class is small.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Read the scenario before looking for a memorised answer.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
Keep practising
Last reviewed: Jun 24, 2026
This MLS-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 MLS-C01 exam.
Question Discussion
Share a tip, memory trick, or ask about the reasoning behind this question. Do not post real exam questions, leaked content, braindumps, or copyrighted exam material. Comments are moderated and may be removed without notice.
Sign in to join the discussion.