The answer is to apply SMOTE to oversample the minority class. This is correct because the dataset is severely imbalanced—only 500 churners out of 10,000 samples—causing logistic regression to bias toward the majority class, which suppresses recall and drags down the F1-score. SMOTE, or Synthetic Minority Oversampling Technique, addresses this by generating synthetic examples of the minority class through interpolation between existing instances, effectively balancing the class distribution and enabling the model to learn a more accurate decision boundary. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this scenario tests your understanding of how to handle class imbalance in binary classification, a frequent topic in questions about model improvement and evaluation metrics. A common trap is choosing cost-sensitive learning or threshold tuning first, but SMOTE directly tackles the root cause of low recall by augmenting the minority class. Memory tip: SMOTE “smooths” the imbalance by creating synthetic samples, not just duplicating 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 training a binary classifier to predict customer churn. The dataset has 10,000 samples, with 500 churners (positive class). The scientist trains a logistic regression model and obtains an F1-score of 0.6. To improve the F1-score, which approach is MOST likely to be effective?
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 minority class
The dataset is highly imbalanced (500 churners out of 10,000 samples, a 5% positive rate). Logistic regression trained on such imbalance tends to bias toward the majority class, resulting in low recall for the minority class and a poor F1-score. SMOTE (Synthetic Minority Oversampling Technique) generates synthetic samples for the minority class by interpolating between existing minority instances, which balances the class distribution and allows the model to learn a better decision boundary, directly improving recall and F1-score.
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 regularization strength (C)
Why it's wrong here
Higher regularization reduces overfitting but does not address class imbalance.
✗
Apply PCA to reduce feature dimensionality
Why it's wrong here
PCA addresses multicollinearity, not class imbalance.
✓
Apply SMOTE to oversample the minority class
Why this is correct
SMOTE generates synthetic samples for the minority class, balancing the dataset and often improving F1-score.
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 the original dataset without any modification
Why it's wrong here
The model is already trained; class imbalance still exists.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Cisco often tests the misconception that regularization (Option A) or dimensionality reduction (Option B) can fix class imbalance, when in fact they address overfitting and noise, not skewed class priors.
Detailed technical explanation
How to think about this question
SMOTE works by selecting a minority class sample, finding its k-nearest neighbors (typically k=5) from the same class, and creating synthetic samples along the line segments connecting the sample to its neighbors. This increases the minority class representation without simple duplication, reducing overfitting risk. In practice, SMOTE is often combined with random undersampling of the majority class (e.g., using SMOTEENN or SMOTETomek) to further clean noisy overlapping regions, but SMOTE alone is a standard first step for imbalanced classification.
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 minority class — The dataset is highly imbalanced (500 churners out of 10,000 samples, a 5% positive rate). Logistic regression trained on such imbalance tends to bias toward the majority class, resulting in low recall for the minority class and a poor F1-score. SMOTE (Synthetic Minority Oversampling Technique) generates synthetic samples for the minority class by interpolating between existing minority instances, which balances the class distribution and allows the model to learn a better decision boundary, directly improving recall and F1-score.
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 →
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 to predict customer churn. The dataset has 10,000 samples with 500 churners (5% positive class). Which TWO techniques should the scientist use to address the class imbalance? (Choose TWO.)
medium
✓ A.Use SMOTE to oversample the minority class
B.Tune the decision threshold after training
C.Randomly undersample the majority class to match minority size
D.Oversample the minority class by duplicating existing samples
✓ E.Set class_weight='balanced' in the classifier
Why A: Option A (SMOTE) generates synthetic samples for the minority class. Option C (class_weight='balanced') adjusts loss function weights. Option B (undersampling majority) can be used but is not always preferred; Option D (oversampling with replacement) may cause overfitting; Option E (threshold tuning) is post-training.
Variation 2. A data scientist is training a binary classification model on an imbalanced dataset (95% negative class, 5% positive class). The model currently achieves 94% accuracy but a recall of only 0.10 on the positive class. Which TWO strategies should the data scientist consider to improve recall without significantly sacrificing precision? (Choose 2.)
medium
A.Undersample the majority class to match the minority class size.
B.Increase the regularization strength to reduce overfitting.
✓ C.Assign higher class weights to the positive class in the loss function.
D.Use a deeper neural network with more layers.
✓ E.Oversample the minority class using SMOTE.
Why C: Oversampling the minority class (option A) increases the number of positive examples, which helps the model learn better decision boundaries for the positive class. Using class weights (option B) penalizes misclassifications of the minority class more heavily, encouraging the model to focus on positive examples. Both techniques directly address class imbalance. Option C (undersampling) may discard useful negative samples and harm performance. Option D (increasing regularization) typically reduces overfitting but does not specifically improve recall. Option E (using a deeper network) may increase overfitting and does not target recall directly.
Variation 3. A data scientist is training a binary classification model on an imbalanced dataset where the positive class represents 1% of the data. The model needs to maximize recall while keeping precision above 0.7. Which sampling strategy should the data scientist use?
medium
A.NearMiss from imbalanced-learn to undersample the majority class based on distance to minority samples.
✓ B.SMOTE from imbalanced-learn to generate synthetic samples for the minority class.
C.RandomUnderSampler from imbalanced-learn to undersample the majority class.
D.TomekLinks from imbalanced-learn to remove overlapping samples.
E.RandomOverSampler from imbalanced-learn to oversample the minority class.
Why B: Option C is correct because SMOTE generates synthetic samples for the minority class, which can improve recall without discarding data. Option A (RandomUnderSampler) may discard too many majority samples, reducing precision. Option B (RandomOverSampler) can cause overfitting. Option D (NearMiss) focuses on hard samples and may reduce recall. Option E (TomekLinks) only removes noisy instances, not addressing imbalance effectively.
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Question Discussion
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