- A
Random undersampling of the majority class
Why wrong: Undersampling discards data and may lose informative examples.
- B
SMOTE (Synthetic Minority Over-sampling Technique)
SMOTE creates synthetic minority samples, balancing the dataset without data loss.
- C
Random oversampling of the minority class
Why wrong: Random oversampling duplicates existing samples, which can lead to overfitting.
- D
Assign higher class weights to the majority class
Why wrong: Higher weights on majority class would worsen recall for the minority.
Question 931 of 1,000
mediumMultiple ChoiceObjective-mapped
MLA-C01 Practice Question: A machine learning team notices that their binary…
This MLA-C01 practice question tests your understanding of mla-c01 exam topics. 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 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?
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
✓
SMOTE (Synthetic Minority Over-sampling Technique)
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.
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.
- ✗
Random undersampling of the majority class
Why it's wrong here
Undersampling discards data and may lose informative examples.
- ✓
SMOTE (Synthetic Minority Over-sampling Technique)
Why this is correct
SMOTE creates synthetic minority samples, balancing the dataset without data loss.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Random oversampling of the minority class
Why it's wrong here
Random oversampling duplicates existing samples, which can lead to overfitting.
- ✗
Assign higher class weights to the majority class
Why it's wrong here
Higher weights on majority class would worsen recall for the minority.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Cisco often tests the distinction between data-level techniques (like SMOTE) and algorithm-level techniques (like class weighting), and the trap here is that candidates mistakenly choose random oversampling (Option C) thinking it improves recall, without realizing that simple duplication does not add new information and can lead to overfitting.
Detailed technical explanation
How to think about this question
SMOTE works by selecting a minority class instance, finding its k-nearest neighbors (typically k=5), and then creating a synthetic sample along the line segment connecting the instance to a randomly chosen neighbor. This interpolation generates plausible new examples that expand the minority class region in feature space, helping the model learn more robust boundaries. In real-world scenarios like fraud detection or rare disease diagnosis, SMOTE is preferred over naive oversampling because it reduces the risk of overfitting while still balancing the class distribution.
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.
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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: SMOTE (Synthetic Minority Over-sampling Technique) — 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.
What should I do if I get this MLA-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.
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Last reviewed: Jul 4, 2026
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