AIF-C01 Fundamentals of AI and ML Practice Question
A company wants to automatically detect anomalies in server metrics. Which algorithm is most appropriate?
⚠ Common exam trap
The AIF-C01 exam often tests the distinction between supervised and unsupervised learning, and the trap here is that candidates may choose XGBoost or Linear SVM because they are familiar with them for classification, forgetting that anomaly detection typically requires a one-class approach when only normal data is available.
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
✓
One-class SVM
One-class SVM is specifically designed for anomaly detection, as it learns a boundary around the normal data points in the feature space and identifies any point falling outside this boundary as an anomaly. This makes it ideal for detecting unusual patterns in server metrics without requiring labeled anomaly examples.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
XGBoost
Why it's wrong here
XGBoost is a supervised gradient boosting algorithm.
- ✓
One-class SVM
Why this is correct
One-class SVM is commonly used for anomaly detection by learning a boundary around normal data.
- ✗
Linear SVM
Why it's wrong here
Linear SVM is a supervised classifier, not suited for unsupervised anomaly detection.
- ✗
K-Means
Why it's wrong here
K-Means groups data into clusters, not specifically for anomaly detection.
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JA
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.