- A
Principal Component Analysis (PCA)
Why wrong: PCA is an unsupervised dimensionality reduction technique.
- B
Autoencoders
Why wrong: Autoencoders are unsupervised neural networks used for representation learning.
- C
Logistic regression
Logistic regression models the probability of a binary outcome, making it a classification algorithm.
- D
K-means
Why wrong: K-means is an unsupervised clustering algorithm.
Question 344 of 1,000
AIF-C01 AI and ML Fundamentals Practice Question
This AIF-C01 practice question tests your understanding of ai and ml fundamentals. 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.
Which of the following is a supervised learning algorithm used for classification tasks?
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Logistic regression
Logistic regression is a supervised learning algorithm because it learns from labeled training data to map input features to discrete class labels. Despite its name, it is used for binary classification tasks (e.g., spam vs. not spam) by applying a logistic (sigmoid) function to output a probability between 0 and 1, then thresholding to assign a class.
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.
- ✗
Principal Component Analysis (PCA)
Why it's wrong here
PCA is an unsupervised dimensionality reduction technique.
- ✗
Autoencoders
Why it's wrong here
Autoencoders are unsupervised neural networks used for representation learning.
- ✓
Logistic regression
Why this is correct
Logistic regression models the probability of a binary outcome, making it a classification algorithm.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
K-means
Why it's wrong here
K-means is an unsupervised clustering algorithm.
Common exam traps
Common exam trap: answer the scenario, not the keyword
AWS often tests the misconception that 'regression' in the name implies it is only for regression tasks, but logistic regression is specifically a classification algorithm, while PCA and K-means are unsupervised and autoencoders are not supervised.
Detailed technical explanation
How to think about this question
Logistic regression models the log-odds of the positive class as a linear combination of input features, then applies the sigmoid function to produce a probability. The decision boundary is linear, making it a generalized linear model (GLM) with a binomial error distribution. In real-world scenarios, it is often used as a baseline classifier for binary outcomes, such as predicting customer churn or medical diagnosis, and can be regularized (L1/L2) to prevent overfitting.
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.
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FAQ
Questions learners often ask
What does this AIF-C01 question test?
AI and ML Fundamentals — This question tests AI and ML Fundamentals — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Logistic regression — Logistic regression is a supervised learning algorithm because it learns from labeled training data to map input features to discrete class labels. Despite its name, it is used for binary classification tasks (e.g., spam vs. not spam) by applying a logistic (sigmoid) function to output a probability between 0 and 1, then thresholding to assign a class.
What should I do if I get this AIF-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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Last reviewed: Jul 4, 2026
This AIF-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 AIF-C01 exam.
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