The answer is to apply SMOTE to oversample the rare class in the training data. SMOTE, or Synthetic Minority Oversampling Technique, improves recall for rare classes in multi-class classification by generating synthetic samples through interpolation between existing minority instances, directly addressing the class imbalance without discarding valuable majority data. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this question tests your understanding of how to boost recall for an underrepresented class while preserving overall accuracy—a common scenario when a model achieves high accuracy but fails on a rare but critical class. A frequent trap is choosing random undersampling of the majority class, which can discard useful data and reduce overall accuracy, or simply duplicating the rare class, which leads to overfitting. For a memory tip, recall that SMOTE “smooths” the imbalance by creating new, plausible minority examples, not just copying them.
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 building a multi-class classification model with 10 classes. The dataset has 100,000 samples. After training a random forest with 100 trees, the model achieves 85% accuracy on the test set. However, the data scientist notices that for one rare class (1% of data), recall is only 5%. Which technique is MOST likely to improve recall for the rare class without significantly reducing overall accuracy?
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
✓
Apply SMOTE to oversample the rare class in the training data
SMOTE (Synthetic Minority Oversampling Technique) generates synthetic samples for the rare class by interpolating between existing minority instances, which directly addresses the class imbalance. This increases the model's exposure to the rare class during training, improving recall without discarding data or significantly altering the overall class distribution, thus preserving 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.
✗
Increase the number of trees to 500
Why it's wrong here
More trees may not help with class imbalance.
✓
Apply SMOTE to oversample the rare class in the training data
Why this is correct
SMOTE creates synthetic samples for the minority class.
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.
✗
Use stratified sampling only for the test set
Why it's wrong here
Stratified sampling in test set ensures representation but does not improve training.
✗
Reduce the decision threshold for the rare class to 0.1
Why it's wrong here
Changing threshold may increase recall but decrease precision and overall accuracy.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Cisco often tests the misconception that increasing model complexity (more trees) or adjusting thresholds post-training can fix class imbalance, when in fact the root cause is the skewed training data distribution, which requires a data-level technique like SMOTE.
Detailed technical explanation
How to think about this question
SMOTE works by selecting a minority instance, finding its k-nearest neighbors (typically k=5), and creating synthetic samples along the line segments connecting the instance to its neighbors. This avoids the overfitting problem of simple random oversampling and is particularly effective for tree-based models like random forests, which can benefit from the additional decision boundaries. In practice, SMOTE should be applied only to the training set after a train-test split to avoid data leakage.
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 cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
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: Apply SMOTE to oversample the rare class in the training data — SMOTE (Synthetic Minority Oversampling Technique) generates synthetic samples for the rare class by interpolating between existing minority instances, which directly addresses the class imbalance. This increases the model's exposure to the rare class during training, improving recall without discarding data or significantly altering the overall class distribution, thus preserving 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.
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
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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 machine learning team is building a multi-class image classifier using a pre-trained ResNet-50 model in Amazon SageMaker. The dataset has 10 classes but is highly imbalanced, with one class representing 80% of the samples. The team wants to improve model performance on the minority classes. Which TWO of the following approaches are most likely to help? (Select TWO.)
hard
✓ A.Oversample the minority classes in the training data.
B.Reduce the batch size to increase the frequency of weight updates.
C.Increase the number of layers in the model.
D.Switch to a focal loss function.
✓ E.Use class weighting in the loss function.
Why A: Oversampling the minority classes (Option A) directly addresses class imbalance by replicating samples from underrepresented classes, giving the model more exposure to them during training. This is a standard data-level technique that helps the ResNet-50 model learn discriminative features for minority classes without altering the loss function or model architecture.
Last reviewed: Jun 11, 2026
Question Discussion
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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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