- A
Undersample the majority class to match the minority class size.
Why wrong: Undersampling discards many negative samples, potentially losing useful information.
- B
Increase the regularization strength to reduce overfitting.
Why wrong: Regularization does not specifically address class imbalance or recall.
- C
Assign higher class weights to the positive class in the loss function.
Higher weight for positive class penalizes false negatives, improving recall.
- D
Use a deeper neural network with more layers.
Why wrong: Adding layers may increase capacity but does not target recall improvement for imbalanced data.
- E
Oversample the minority class using SMOTE.
SMOTE generates synthetic positive samples, balancing the dataset and improving recall.
Question 826 of 1,755
Class Imbalance Handling Techniques for AWS ML Specialty
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 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.)
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Assign higher class weights to the positive class in the loss function.
Assigning higher class weights to the positive class in the loss function (option C) penalizes misclassifications of the minority class more heavily, forcing the model to focus on positive examples. Oversampling the minority class using SMOTE (option E) generates synthetic positive samples, improving the model's ability to learn decision boundaries for the positive class. Both techniques directly address class imbalance without discarding data. Option A (undersampling) may remove useful negative samples, harming overall performance. Option B (increasing regularization) reduces overfitting but does not specifically improve recall. Option D (using a deeper network) may increase overfitting and does not target recall directly.
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.
- ✗
Undersample the majority class to match the minority class size.
Why it's wrong here
Undersampling discards many negative samples, potentially losing useful information.
- ✗
Increase the regularization strength to reduce overfitting.
Why it's wrong here
Regularization does not specifically address class imbalance or recall.
- ✓
Assign higher class weights to the positive class in the loss function.
Why this is correct
Higher weight for positive class penalizes false negatives, improving recall.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Use a deeper neural network with more layers.
Why it's wrong here
Adding layers may increase capacity but does not target recall improvement for imbalanced data.
- ✓
Oversample the minority class using SMOTE.
Why this is correct
SMOTE generates synthetic positive samples, balancing the dataset 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
A healthcare organisation deploys an application with a public-facing web tier and a private database tier. The database subnet has no public IP and only accepts connections from the web tier's security group. Questions like this test whether you can design cloud network isolation using VNets/VPCs, subnets, and security group rules.
What to study next
Got this wrong? Here's your next step.
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FAQ
Questions learners often ask
What does this MLS-C01 question test?
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: Assign higher class weights to the positive class in the loss function. — Assigning higher class weights to the positive class in the loss function (option C) penalizes misclassifications of the minority class more heavily, forcing the model to focus on positive examples. Oversampling the minority class using SMOTE (option E) generates synthetic positive samples, improving the model's ability to learn decision boundaries for the positive class. Both techniques directly address class imbalance without discarding data. Option A (undersampling) may remove useful negative samples, harming overall performance. Option B (increasing regularization) reduces overfitting but does not specifically improve recall. Option D (using a deeper network) may increase overfitting and does not target recall directly.
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 →
Same concept, more angles
2 more ways this is tested on MLS-C01
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 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?
medium- A.Increase the regularization strength (C)
- B.Apply PCA to reduce feature dimensionality
- ✓ C.Apply SMOTE to oversample the minority class
- D.Use the original dataset without any modification
Why C: 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.
Variation 2. 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, effectively balancing the dataset. Option E (class_weight='balanced') adjusts the loss function to penalize misclassifications of the minority class more heavily. Option B (tuning threshold after training) is a post-processing step, not a technique to address imbalance during training. Option C (random undersampling) can discard useful data, leading to loss of information. Option D (oversampling by duplication) can cause overfitting due to repeated copies of the same samples.
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Last reviewed: Jun 20, 2026
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