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 financial services company is building a model to detect fraudulent credit card transactions. The dataset contains 1 million transactions, with only 0.1% labeled as fraud. The data scientist trains a logistic regression model on the raw dataset and obtains the following results on a held-out test set: accuracy = 99.8%, precision = 50%, recall = 60%, F1 = 0.545. The business requirement is to maximize recall while keeping precision above 80%. Which course of action should the data scientist take to improve the model?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Train the model with class weights inversely proportional to class frequencies
The correct answer is C because assigning class weights inversely proportional to class frequencies penalizes misclassifications of the minority class (fraud) more heavily during training. This directly addresses the severe class imbalance (0.1% fraud) by forcing the logistic regression model to learn decision boundaries that improve recall, while the weight ratio can be tuned to maintain precision above 80%. Unlike naive resampling, this approach preserves the original data distribution and avoids information loss.
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.
✗
Use random undersampling of the majority class to balance the dataset
Why it's wrong here
Undersampling discards data and may reduce model performance, especially with a large majority class.
✗
Collect more historical transaction data and retrain the model
Why it's wrong here
More data might help, but it's not guaranteed to improve precision dramatically, and the timeline is uncertain.
✓
Train the model with class weights inversely proportional to class frequencies
Why this is correct
Class weights help the model focus on the minority class, often improving precision and recall.
Related concept
Read the scenario before looking for a memorised answer.
✗
Apply L2 regularization with a higher penalty to reduce overfitting
Why it's wrong here
Regularization reduces overfitting but does not directly address class imbalance or precision.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Cisco often tests the misconception that resampling (undersampling or oversampling) is always the best first step for imbalance, when in fact cost-sensitive learning via class weights is often more effective and stable for linear models like logistic regression.
Detailed technical explanation
How to think about this question
Logistic regression optimizes log-loss, and without class weights, the model minimizes overall error by focusing on the majority class (99.9% legitimate). By setting class_weight='balanced' in scikit-learn (or manually computing weights as n_samples / (n_classes * np.bincount(y))), the loss contribution from each fraudulent transaction is amplified by a factor of ~1000, shifting the decision threshold to favor recall. The precision-recall trade-off can be further fine-tuned by adjusting the classification threshold after training, but class weighting directly influences the learned coefficients during optimization.
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: Train the model with class weights inversely proportional to class frequencies — The correct answer is C because assigning class weights inversely proportional to class frequencies penalizes misclassifications of the minority class (fraud) more heavily during training. This directly addresses the severe class imbalance (0.1% fraud) by forcing the logistic regression model to learn decision boundaries that improve recall, while the weight ratio can be tuned to maintain precision above 80%. Unlike naive resampling, this approach preserves the original data distribution and avoids information loss.
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.
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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