Quick Answer
The answer is Naive Bayes, which is the best algorithm for text classification in this scenario because it leverages Bayes’ theorem with a strong independence assumption to model the probability of each category given the document’s word features. This makes it highly effective for high-dimensional sparse data like bag-of-words or TF-IDF representations, and it requires relatively little training data to estimate parameters—ideal when you have 10,000 documents across five categories. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this question tests your understanding of when to choose Naive Bayes over alternatives like logistic regression or SVM, often in the context of multi-class document categorization. A common trap is to pick a more complex model like neural networks, but the exam emphasizes that Naive Bayes excels with moderate-sized, high-dimensional text data due to its efficiency and strong baseline performance. Memory tip: think “Bayes for bags”—Naive Bayes is the go-to for bag-of-words text tasks.
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. The dataset contains 10,000 documents, each labeled with one of 5 categories. Which algorithm is most suitable for this task?
Principal Component Analysis (PCA)
Why wrong: PCA is for dimensionality reduction, not classification.
Naive Bayes
Naive Bayes is effective for text classification and small datasets.
Linear regression
Why wrong: Linear regression is for regression, not classification.
k-means clustering
Why wrong: k-means is unsupervised and not for classification.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Naive Bayes
Naive Bayes is highly suitable for text classification because it models the probability of each category given the document's word features using Bayes' theorem with a strong independence assumption. It performs well on high-dimensional sparse data like bag-of-words or TF-IDF representations, and it is particularly effective when the number of documents (10,000) is moderate relative to the vocabulary size, as it requires relatively little training data to estimate parameters.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
Principal Component Analysis (PCA)
Why it's wrong here
PCA is for dimensionality reduction, not classification.
Naive Bayes
Why this is correct
Naive Bayes is effective for text classification and small datasets.
Related concept
Read the scenario before looking for a memorised answer.
Linear regression
Why it's wrong here
Linear regression is for regression, not classification.
k-means clustering
Why it's wrong here
k-means is unsupervised and not for classification.
Common exam traps
Cisco often tests the distinction between supervised and unsupervised learning, leading candidates to mistakenly choose k-means clustering (an unsupervised method) for a labeled classification task, or to confuse PCA with a classification algorithm because it is used for feature reduction before modeling.
Detailed technical explanation
Naive Bayes classifiers, particularly the Multinomial variant, work by computing class-conditional probabilities of word occurrences using Laplace smoothing to handle zero-frequency features. The model assumes that each word's occurrence is independent of others given the class, which is rarely true in practice but still yields robust performance for tasks like spam detection or topic categorization. In real-world scenarios, Naive Bayes often serves as a strong baseline that is fast to train and easy to interpret, especially when the dataset has a balanced class distribution and the text is not highly nuanced.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
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
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FAQ
Modeling — This question tests Modeling — Read the scenario before looking for a memorised answer..
The correct answer is: Naive Bayes — Naive Bayes is highly suitable for text classification because it models the probability of each category given the document's word features using Bayes' theorem with a strong independence assumption. It performs well on high-dimensional sparse data like bag-of-words or TF-IDF representations, and it is particularly effective when the number of documents (10,000) is moderate relative to the vocabulary size, as it requires relatively little training data to estimate parameters.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 24, 2026
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.
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