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AIF-C01 Practice Question: Which TWO of the following are types of…
Which TWO of the following are types of unsupervised learning? (Select TWO.)
⚠ Common exam trap
The AWS AI Practitioner exam often tests the distinction between supervised and unsupervised learning by presenting classification and regression as plausible unsupervised options, exploiting the common misconception that any 'grouping' or 'reduction' task is unsupervised, while in fact classification and regression require labeled data.
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
✓
Dimensionality reduction
Dimensionality reduction (B) is a type of unsupervised learning because it seeks to compress or transform unlabeled data into a lower-dimensional representation (e.g., via PCA or t-SNE) without any target labels. Clustering (C) is also unsupervised, as algorithms like k-means or DBSCAN group unlabeled data points by similarity without predefined classes. Classification (A) and regression (E) are supervised learning tasks, since they require labeled training data with discrete or continuous target values, respectively. Reinforcement learning (D) is a separate paradigm in which an agent learns from reward signals via interaction with an environment, not from unlabeled data in the unsupervised sense.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Classification
Why it's wrong here
Classification is supervised learning: it maps labelled inputs to discrete categories, so it requires ground-truth targets. It is the correct approach when historical data already carries class labels, such as spam detection or image categorisation.
- ✓
Dimensionality reduction
Why this is correct
Dimensionality reduction is unsupervised because it finds structure in unlabelled data, projecting high-dimensional inputs onto fewer components without target labels. Techniques such as principal component analysis learn patterns purely from feature distributions, unlike supervised classification or regression.
- ✓
Clustering
Why this is correct
Clustering groups unlabelled data points by similarity, discovering inherent structure without target labels — the defining trait of unsupervised learning. It satisfies the question's requirement by partitioning data into clusters via algorithms such as k-means, unlike supervised tasks that map inputs to known labels.
- ✗
Reinforcement learning
Why it's wrong here
Reinforcement learning trains an agent through reward signals from environment interaction, not by finding structure in unlabelled data. It is the right paradigm for sequential decision-making tasks such as game playing or robot control, where no labelled dataset exists.
- ✗
Regression
Why it's wrong here
Regression predicts a continuous numeric output from labelled training data, so it belongs to supervised learning and cannot be an unsupervised type. It is tempting because regression also uncovers patterns and relationships within data, and it would be the correct choice when the scenario requires forecasting a value, such as predicting house prices from historical labelled examples.
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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.