The answer is feature selection to remove rare words, regularization (L1 or L2), and lowering the n-gram range. These three techniques directly reduce model complexity, which is the root cause of overfitting in bag-of-words text classification: rare words act as noise that the model memorizes, regularization penalizes overly large coefficients, and a lower n-gram range limits the number of feature combinations the model can learn. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this question tests your understanding of how to combat overfitting specifically in high-dimensional, sparse text data—a common trap is confusing TF-IDF weighting with regularization, but TF-IDF merely rescales term frequencies and does not penalize complexity. A strong memory tip is “Rare, Regularize, Range”: remove rare words, apply L1/L2 regularization, and restrict the n-gram range to prevent the model from fitting to spurious patterns.
MLS-C01 Modeling Practice Question
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. The dataset contains 100,000 documents with a vocabulary of 50,000 unique words. The model is overfitting. Which THREE techniques can help reduce overfitting? (Choose THREE.)
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Apply L1 or L2 regularization
Feature selection (removing rare words), regularization (L1/L2), and lowering n-gram range reduce model complexity and overfitting. Option A (increasing max_features) can increase overfitting. Option D (using TF-IDF) is a weighting scheme, not a regularization technique.
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 max_features to include more words
Why it's wrong here
Adding more features increases model complexity and overfitting risk.
✓
Apply L1 or L2 regularization
Why this is correct
Regularization penalizes large coefficients, reducing overfitting.
Related concept
Read the scenario before looking for a memorised answer.
✓
Reduce the n-gram range to unigrams only
Why this is correct
Lower n-gram range reduces feature space and overfitting.
Related concept
Read the scenario before looking for a memorised answer.
✓
Use feature selection to remove rare words
Why this is correct
Removing rare words reduces noise and overfitting.
Related concept
Read the scenario before looking for a memorised answer.
✗
Use TF-IDF instead of raw counts
Why it's wrong here
TF-IDF is a feature scaling method, not a regularization technique.
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 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: Apply L1 or L2 regularization — Feature selection (removing rare words), regularization (L1/L2), and lowering n-gram range reduce model complexity and overfitting. Option A (increasing max_features) can increase overfitting. Option D (using TF-IDF) is a weighting scheme, not a regularization technique.
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.
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 →
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 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?
medium
A.Increase the number of n-grams features
B.Use one-hot encoding instead of bag-of-words
C.Use a more complex model such as a neural network
✓ D.Reduce the vocabulary size by removing rare and very frequent terms
✓ E.Apply L2 regularization to the logistic regression model
Why D: 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.
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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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