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 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.
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 algorithm.
- ✓
XGBoost
Why this is correct
XGBoost is a supervised gradient boosting algorithm.
- ✓
Linear Learner
Why this is correct
Linear Learner is a supervised algorithm for regression and classification.
- ✗
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.
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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.