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 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.
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.
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.
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.
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. — 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.
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.
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Question Discussion
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