The answer is to set scale_pos_weight to the ratio of negative to positive samples. This parameter directly increases the penalty for misclassifying the minority positive class, amplifying its gradient contribution during training and shifting the decision boundary to improve recall. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this question tests your understanding of how XGBoost handles imbalanced data through weighted loss functions, often appearing in scenarios where SageMaker’s built-in algorithm is used for fraud detection or rare event prediction. A common trap is choosing a value like the inverse of the imbalance ratio or simply setting it to 1, which fails to correct the class skew. For a dataset with 0.1% positives, the correct ratio is roughly 999:1, balancing recall gains without excessive false positives. Memory tip: think “weight the minority higher to catch more positives”—scale_pos_weight = negative count divided by positive count.
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 on a highly imbalanced dataset (0.1% positive class). To improve recall, the team decides to use SageMaker's built-in XGBoost algorithm. Which parameter adjustment is most likely to increase recall without significantly sacrificing precision?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue: "most likely"
Why it matters: Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
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
Setting scale_pos_weight to the ratio of negative to positive samples (approximately 999:1) tells XGBoost to assign a higher penalty to misclassifications of the minority positive class. This directly increases the gradient contribution from positive samples during training, which shifts the decision boundary to improve recall while maintaining a balance that avoids excessive false positives, thus preserving precision.
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.
✗
Increase max_depth from 5 to 10
Why it's wrong here
Increasing max_depth may cause overfitting but does not specifically improve recall on imbalanced data.
✗
Reduce num_round from 100 to 50
Why it's wrong here
Reducing num_round typically reduces model capacity and can hurt recall.
✗
Increase subsample from 0.8 to 1.0
Why it's wrong here
Increasing subsample uses more data per iteration but does not directly target class imbalance.
✓
Set scale_pos_weight to the ratio of negative to positive samples
Why this is correct
scale_pos_weight adjusts class weights to focus on the minority class, improving recall.
Clue confirmation
The clue word "most likely" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Cisco often tests the misconception that simply increasing model complexity (max_depth) or data usage (subsample) will fix imbalance, when the correct approach is to use a class-weighting parameter like scale_pos_weight that directly addresses the skewed gradient contributions.
Detailed technical explanation
How to think about this question
Under the hood, scale_pos_weight modifies the loss function by scaling the gradient and hessian for positive class samples, effectively performing cost-sensitive learning. In XGBoost, this parameter is equivalent to setting the sum of negative instance weights to the sum of positive instance weights, which balances the gradient contributions. In a real-world fraud detection scenario with 0.1% fraud rate, this adjustment can boost recall from near zero to over 80% while keeping precision above 90%, depending on the threshold tuning.
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.
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 — Setting scale_pos_weight to the ratio of negative to positive samples (approximately 999:1) tells XGBoost to assign a higher penalty to misclassifications of the minority positive class. This directly increases the gradient contribution from positive samples during training, which shifts the decision boundary to improve recall while maintaining a balance that avoids excessive false positives, thus preserving precision.
What should I do if I get this MLS-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: "most likely". Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
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 →
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.
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.