- A
Undersample the majority class in the training set to match the minority class size.
Why wrong: Undersampling discards a large number of samples, potentially losing valuable information and harming model performance.
- B
Oversample the minority class in the training set using SMOTE, and keep the test set as is.
SMOTE on the training set only addresses class imbalance during training; the test set preserves the original distribution for a realistic assessment.
- C
Randomly oversample the minority class in the entire dataset and then split.
Why wrong: Random oversampling before splitting can cause the same original samples to appear in both training and test sets, leading to data leakage.
- D
Apply SMOTE to the entire dataset before splitting into training and test sets.
Why wrong: Applying SMOTE before splitting would cause synthetic samples to leak into the test set, leading to an unrealistic evaluation.
SMOTE for Class Imbalance in Binary Classification
This MLA-C01 practice question tests your understanding of mla-c01 exam topics. Compare every option against the stated constraints before choosing — the best answer satisfies all requirements, not just the most obvious one. 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 preparing a dataset for a binary classification model. The dataset has 10,000 samples, but the positive class represents only 2% of the data. The data scientist needs to train a model that will be evaluated on a hold-out test set that preserves the original class distribution. Which data preparation strategy is MOST appropriate?
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
Oversample the minority class in the training set using SMOTE, and keep the test set as is.
SMOTE generates synthetic samples for the minority class to balance the training set, while keeping the test set realistic with the original class distribution. Oversampling the test set would give an overly optimistic evaluation, and undersampling the majority class in training may lose useful information. Class weights are a modeling technique, not a data preparation step.
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 in the training set to match the minority class size.
Why it's wrong here
Undersampling discards a large number of samples, potentially losing valuable information and harming model performance.
- ✓
Oversample the minority class in the training set using SMOTE, and keep the test set as is.
Why this is correct
SMOTE on the training set only addresses class imbalance during training; the test set preserves the original distribution for a realistic assessment.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Randomly oversample the minority class in the entire dataset and then split.
Why it's wrong here
Random oversampling before splitting can cause the same original samples to appear in both training and test sets, leading to data leakage.
- ✗
Apply SMOTE to the entire dataset before splitting into training and test sets.
Why it's wrong here
Applying SMOTE before splitting would cause synthetic samples to leak into the test set, leading to an unrealistic evaluation.
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 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 MLA-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.
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FAQ
Questions learners often ask
What does this MLA-C01 question test?
Read the scenario before looking for a memorised answer.
What is the correct answer to this question?
The correct answer is: Oversample the minority class in the training set using SMOTE, and keep the test set as is. — SMOTE generates synthetic samples for the minority class to balance the training set, while keeping the test set realistic with the original class distribution. Oversampling the test set would give an overly optimistic evaluation, and undersampling the majority class in training may lose useful information. Class weights are a modeling technique, not a data preparation step.
What should I do if I get this MLA-C01 question wrong?
Identify which MLA-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
4 more ways this is tested on MLA-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 needs to prepare a dataset for a binary classification model. The dataset has 10,000 samples with 100 features, and the target class is highly imbalanced (5% positive). Which combination of techniques should the data scientist use to address class imbalance and prepare the data for training?
medium- A.Use random undersampling of the majority class, then apply MinMaxScaler to all features.
- B.Use RandomOverSampler to duplicate minority samples, then perform a random train-test split.
- ✓ C.Apply SMOTE to oversample the minority class, then use stratified splitting for train/test sets.
- D.Apply Lasso regression for feature selection, then use k-fold cross-validation without stratification.
Why C: SMOTE generates synthetic samples for the minority class, and stratified splitting ensures the same class proportion in train/test sets. Lasso regression can help with feature selection but is not directly for imbalance. Undersampling would discard too many majority samples. Random splitting without stratification could lead to uneven class distribution.
Variation 2. A machine learning team is preparing data for a binary classification model. The target variable has a severe class imbalance (95% negative, 5% positive). The team wants to train a model that maximizes recall on the positive class while keeping training time manageable. Which approach should they use?
medium- A.Use class weights inversely proportional to class frequencies
- ✓ B.Use SMOTE to generate synthetic samples for the positive class
- C.Oversample the positive class by simply duplicating existing records
- D.Undersample the majority class to match the minority class size
Why B: SMOTE (Synthetic Minority Oversampling Technique) creates synthetic samples of the minority class, increasing its representation without simply duplicating data. This often improves recall without drastically increasing training time compared to other methods.
Variation 3. A machine learning engineer is preparing a dataset for binary classification. The target variable has a severe class imbalance (95% negative, 5% positive). Which technique can help address this imbalance during data preparation?
easy- A.L1 Regularization (Lasso)
- B.StandardScaler
- C.Principal Component Analysis (PCA)
- ✓ D.SMOTE (Synthetic Minority Over-sampling Technique)
Why D: SMOTE (Synthetic Minority Over-sampling Technique) generates synthetic samples for the minority class, which is a common approach to handle class imbalance. Options A, B, and D are not standard techniques for class imbalance.
Variation 4. A machine learning team notices that their binary classification model has high accuracy but low recall on the minority class. The dataset has 10% positive examples and 90% negative examples. Which technique should they apply to improve recall without discarding data?
medium- A.Random undersampling of the majority class
- ✓ B.SMOTE (Synthetic Minority Over-sampling Technique)
- C.Random oversampling of the minority class
- D.Assign higher class weights to the majority class
Why B: SMOTE (Synthetic Minority Over-sampling Technique) is the correct choice because it generates synthetic examples for the minority class by interpolating between existing minority instances and their k-nearest neighbors. This increases the representation of the positive class without simply duplicating data, which helps the model learn better decision boundaries and improves recall without discarding any original data.
Last reviewed: Jul 4, 2026
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