The answer is to use SMOTE to generate synthetic samples of the minority class and to set the scale_pos_weight parameter in XGBoost to adjust for class imbalance. These two actions directly target the root cause of poor recall: the model’s bias toward the majority class. SMOTE creates artificial positive examples to balance the training data, while scale_pos_weight increases the penalty for misclassifying the positive class, forcing the model to focus on recall. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this scenario tests your understanding of built-in XGBoost hyperparameters versus preprocessing techniques—a common trap is confusing scale_pos_weight with subsampling or learning rate adjustments. Remember that scale_pos_weight is a parameter, not a data modification, and SMOTE is a data-level fix; together they improve recall without the information loss of random undersampling. A useful mnemonic is “Weight the class, then oversample the mass” to recall that scale_pos_weight adjusts the loss function, while SMOTE augments the dataset.
MLS-C01 Modeling Practice Question
This MLS-C01 practice question tests your understanding of modeling. 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 classification model using Amazon SageMaker's built-in XGBoost algorithm. The dataset is highly imbalanced (95% negative class, 5% positive class). The model achieves high accuracy but poor recall on the positive class. Which TWO actions should the data scientist take to improve recall without significantly sacrificing precision?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Set scale_pos_weight to the ratio of negative to positive samples.
Options B and E are correct. Using scale_pos_weight adjusts the weight of the positive class, directly addressing imbalance. SMOTE oversamples the minority class to balance the dataset. Option A is wrong because subsampling the majority class may lose information. Option C is wrong because increasing max_depth may overfit. Option D is wrong because reducing eta may slow convergence but not directly help imbalance.
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.
✗
Perform random undersampling of the majority class.
Why it's wrong here
Undersampling can discard useful data and reduce precision.
✓
Set scale_pos_weight to the ratio of negative to positive samples.
Why this is correct
This parameter assigns higher weight to the minority class, penalizing misclassifications more.
Related concept
Read the scenario before looking for a memorised answer.
✗
Increase the max_depth hyperparameter.
Why it's wrong here
Increasing depth can lead to overfitting, which may harm generalization on minority class.
✗
Reduce the learning rate (eta) and increase num_round.
Why it's wrong here
This may improve convergence but does not specifically address class imbalance.
✓
Use SMOTE to generate synthetic samples of the minority class.
Why this is correct
SMOTE creates synthetic examples of the minority class, balancing the training set and improving recall.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Many certification questions include familiar terms but test a specific constraint. Read the exact wording before choosing an answer that is generally true but wrong for this case.
Detailed technical explanation
How to think about this question
This question should be treated as a scenario, not a definition check. Identify the problem, the constraint and the best action. Then compare each option against those facts.
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.
Use explanations to understand the rule behind the answer.
TExam Day Tips
→Underline the problem statement mentally.
→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 MLS-C01 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
Modeling — This question tests Modeling — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Set scale_pos_weight to the ratio of negative to positive samples. — Options B and E are correct. Using scale_pos_weight adjusts the weight of the positive class, directly addressing imbalance. SMOTE oversamples the minority class to balance the dataset. Option A is wrong because subsampling the majority class may lose information. Option C is wrong because increasing max_depth may overfit. Option D is wrong because reducing eta may slow convergence but not directly help imbalance.
What should I do if I get this MLS-C01 question wrong?
Identify which MLS-C01 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
What is the key concept behind this question?
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 →
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 data scientist is training a binary classification model using Amazon SageMaker's XGBoost. The dataset is highly imbalanced (99% negative class, 1% positive class). The data scientist wants to maximize the F1-score. Which parameter adjustment is most appropriate?
easy
A.Set max_depth to 10
B.Set eta to 0.01
C.Set subsample to 0.5
✓ D.Set scale_pos_weight to 99
Why D: Setting scale_pos_weight to 99 is the most appropriate adjustment because it directly addresses class imbalance by assigning a higher weight to the minority (positive) class during training. In XGBoost, scale_pos_weight controls the balance of positive and negative weights, typically set as sum(negative instances) / sum(positive instances), which here is 99/1 = 99. This forces the model to penalize misclassifications of the positive class more heavily, thereby improving recall and F1-score.
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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
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