The answer is resampling the training data to balance class representation. This is the most effective approach because class imbalance in multiclass classification causes models like XGBoost to optimize for overall accuracy, often at the expense of minority class recall. By oversampling the minority class or undersampling majority classes, you force the model to learn more robust decision boundaries for the underperforming class, directly addressing the root cause of low recall without a significant accuracy drop. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this scenario tests your understanding of data-level techniques versus algorithm-level fixes; a common trap is choosing to adjust class weights in XGBoost’s hyperparameters, but resampling is more reliable for built-in objectives like multi:softmax. Remember: when recall for one class lags, resample the data first—think of it as “rebalancing before reweighting.”
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 team trained a multiclass classification model using SageMaker built-in XGBoost. The model's accuracy is high, but for a specific class, recall is very low. The team wants to improve recall for that class without significant accuracy drop. Which approach is MOST effective?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Resample the training data to balance the class representation
Option B is correct because resampling the training data to balance class representation directly addresses the root cause of low recall for a specific class in a multiclass XGBoost model. XGBoost's built-in objective functions (e.g., 'multi:softmax') optimize for overall accuracy, which can bias the model toward majority classes; resampling (e.g., oversampling the minority class or undersampling the majority) forces the model to learn decision boundaries that better capture the minority class, improving recall without drastically reducing overall accuracy.
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.
✗
Add more training data from all classes
Why it's wrong here
Adding more data may not balance the class.
✓
Resample the training data to balance the class representation
Why this is correct
Resampling addresses class imbalance, improving recall for minority class.
Related concept
Read the scenario before looking for a memorised answer.
✗
Increase the max_depth hyperparameter of XGBoost
Why it's wrong here
Increasing max_depth may cause overfitting, not specifically improve recall for a class.
✗
Switch from XGBoost to a linear learner
Why it's wrong here
Linear learner may not handle complex patterns.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often assume increasing model complexity (max_depth) or switching algorithms will fix class imbalance, when in fact the most effective and direct approach is to rebalance the training data through resampling.
Detailed technical explanation
How to think about this question
Under the hood, XGBoost uses gradient boosting with second-order gradient statistics; when classes are imbalanced, the loss function (e.g., cross-entropy) is dominated by majority class gradients, causing splits to favor majority class separation. Resampling techniques like SMOTE (Synthetic Minority Oversampling Technique) or random undersampling alter the gradient distribution, effectively giving the minority class more weight in each boosting round. In real-world scenarios like fraud detection or rare disease diagnosis, resampling is a standard first step before adjusting class weights or using specialized loss functions like focal loss.
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.
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: Resample the training data to balance the class representation — Option B is correct because resampling the training data to balance class representation directly addresses the root cause of low recall for a specific class in a multiclass XGBoost model. XGBoost's built-in objective functions (e.g., 'multi:softmax') optimize for overall accuracy, which can bias the model toward majority classes; resampling (e.g., oversampling the minority class or undersampling the majority) forces the model to learn decision boundaries that better capture the minority class, improving recall without drastically reducing overall accuracy.
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.
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 multiclass classification model to categorize support tickets into 50 categories. The dataset has 100,000 labeled tickets. The scientist uses a random forest classifier with 100 trees. The model achieves 90% accuracy on the test set, but the F1-score for some rare categories is below 0.1. The scientist wants to improve performance on rare categories without significantly reducing overall accuracy. Which approach should the scientist try?
medium
A.Increase the maximum depth of trees
B.Reduce the number of trees to 50 to prevent overfitting
C.Switch to a one-vs-rest logistic regression model
✓ D.Use class_weight='balanced' or compute custom class weights
Why D: Option A (use class weights) helps the model focus on rare classes. Option B (reduce the number of trees) may hurt overall performance. Option C (use one-vs-rest logistic regression) may not handle rare classes well. Option D (increase max_depth) could overfit.
Last reviewed: Jun 24, 2026
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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.
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