This AIF-C01 practice question tests your understanding of fundamentals of ai and ml. 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 TWO of the following are examples of supervised learning tasks that can be performed using Amazon SageMaker built-in algorithms?
Principal Component Analysis (PCA)
Why wrong: PCA is an unsupervised dimensionality reduction algorithm.
XGBoost
XGBoost is a supervised gradient boosting algorithm.
Linear Learner
Linear Learner is a supervised algorithm for regression and classification.
Latent Dirichlet Allocation (LDA)
Why wrong: LDA is an unsupervised topic modeling algorithm.
K-Means
Why wrong: K-Means is an unsupervised clustering algorithm.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
XGBoost
XGBoost is a supervised learning algorithm that uses gradient-boosted decision trees for regression, classification, and ranking tasks. Amazon SageMaker's built-in XGBoost algorithm is optimized for distributed training and directly supports labeled training data, making it a correct example of a supervised learning task.
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 an unsupervised dimensionality reduction algorithm.
XGBoost
Why this is correct
XGBoost is a supervised gradient boosting algorithm.
Related concept
Read the scenario before looking for a memorised answer.
Linear Learner
Why this is correct
Linear Learner is a supervised algorithm for regression and classification.
Related concept
Read the scenario before looking for a memorised answer.
Latent Dirichlet Allocation (LDA)
Why it's wrong here
LDA is an unsupervised topic modeling algorithm.
K-Means
Why it's wrong here
K-Means is an unsupervised clustering algorithm.
Common exam traps
Cisco often tests the distinction between supervised and unsupervised learning by listing algorithms like PCA, LDA, and K-Means alongside supervised ones, trapping candidates who recognize the algorithm names but forget their learning paradigm.
Detailed technical explanation
Supervised learning requires labeled input-output pairs to learn a mapping function. SageMaker's built-in XGBoost uses a gradient boosting framework where each new tree corrects errors of the previous ensemble, and it supports custom objective functions and evaluation metrics. In practice, XGBoost is widely used for tabular data problems like fraud detection or customer churn prediction, where labeled historical data is available.
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
Fundamentals of AI and ML — This question tests Fundamentals of AI and ML — Read the scenario before looking for a memorised answer..
The correct answer is: XGBoost — XGBoost is a supervised learning algorithm that uses gradient-boosted decision trees for regression, classification, and ranking tasks. Amazon SageMaker's built-in XGBoost algorithm is optimized for distributed training and directly supports labeled training data, making it a correct example of a supervised learning task.
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 25, 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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