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 building a text classification model using a bag-of-words approach with logistic regression. The dataset has 10,000 documents and 50,000 unique tokens. The model overfits. Which TWO techniques can help reduce overfitting?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Reduce the vocabulary size by removing rare and very frequent terms
Option D is correct because removing rare and very frequent terms reduces the feature space and eliminates noise, which helps the logistic regression model generalize better. Rare terms often act as noise that the model can latch onto for spurious correlations, while very frequent terms (like stopwords) provide little discriminative power. This dimensionality reduction directly combats overfitting by simplifying the model.
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.
✗
Increase the number of n-grams features
Why it's wrong here
Adding more features increases model complexity and likely overfitting.
✗
Use one-hot encoding instead of bag-of-words
Why it's wrong here
One-hot encoding does not reduce the number of features and can increase sparsity.
✗
Use a more complex model such as a neural network
Why it's wrong here
More complex models increase overfitting risk.
✓
Reduce the vocabulary size by removing rare and very frequent terms
Why this is correct
Reducing the number of features reduces model complexity and overfitting.
Related concept
Read the scenario before looking for a memorised answer.
✓
Apply L2 regularization to the logistic regression model
Why this is correct
L2 regularization penalizes large weights, reducing overfitting.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
AWS often tests the misconception that adding more features or using a more complex model always improves performance, when in fact these actions increase overfitting risk in high-dimensional sparse datasets.
Detailed technical explanation
How to think about this question
L2 regularization (option E) works by adding a penalty proportional to the square of the coefficients to the loss function, which shrinks weights toward zero and prevents any single feature from dominating. Reducing vocabulary size (option D) is a form of feature selection that lowers the model's degrees of freedom, directly addressing the curse of dimensionality in high-dimensional sparse spaces like bag-of-words. In practice, combining both techniques is common: using TF-IDF to filter terms and applying L2 regularization to stabilize the logistic regression coefficients.
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 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 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: Reduce the vocabulary size by removing rare and very frequent terms — Option D is correct because removing rare and very frequent terms reduces the feature space and eliminates noise, which helps the logistic regression model generalize better. Rare terms often act as noise that the model can latch onto for spurious correlations, while very frequent terms (like stopwords) provide little discriminative power. This dimensionality reduction directly combats overfitting by simplifying the model.
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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This MLS-C01 practice question is part of Courseiva's free Amazon Web Services certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the MLS-C01 exam.
Question Discussion
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