AIF-C01 Fundamentals of AI and ML Practice Question
Which TWO of the following are examples of supervised learning tasks that can be performed using Amazon SageMaker built-in algorithms?
⚠ Common exam trap
The AIF-C01 exam 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.
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
✓
XGBoost
XGBoost (B) is correct because Amazon SageMaker's built-in XGBoost algorithm is a supervised gradient-boosted trees implementation used for classification and regression on labeled data. Linear Learner (C) is correct because SageMaker's built-in Linear Learner algorithm trains supervised linear models for classification and regression using labeled datasets. PCA (A) is not correct because it is an unsupervised dimensionality-reduction technique that does not use target labels. LDA (D) is not correct because it is an unsupervised topic-modeling algorithm. K-Means (E) is not correct because it is an unsupervised clustering algorithm that groups unlabeled data.
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 unsupervised dimensionality reduction, finding principal components without labels, so it is not a supervised task. It is tempting because SageMaker ships a built-in PCA algorithm, but that algorithm's purpose is feature reduction, not prediction from labelled data.
- ✓
XGBoost
Why this is correct
XGBoost is a gradient-boosted decision tree algorithm that trains on labelled data to predict a target, making it a supervised task. SageMaker provides it as a built-in algorithm, satisfying the stem's requirement for supervised learning examples.
- ✓
Linear Learner
Why this is correct
Linear Learner fits a linear model to labelled data for classification or regression, which is supervised learning by definition. It is offered as a SageMaker built-in algorithm, directly satisfying the stem's requirement for supervised learning examples.
- ✗
Latent Dirichlet Allocation (LDA)
Why it's wrong here
LDA is unsupervised topic modelling over documents, discovering latent topics without labels, so it is not supervised. It is tempting because SageMaker provides a built-in LDA algorithm, but its output is topic distributions, not predictions trained on labelled examples.
- ✗
K-Means
Why it's wrong here
K-Means is unsupervised clustering, grouping unlabelled points by distance to centroids, so it is not a supervised task. It is tempting because SageMaker includes a built-in K-Means algorithm, but clustering discovers structure rather than learning from labelled targets.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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.