Quick Answer
The answer is one-class SVM, the most appropriate algorithm for automatically detecting anomalies in server metrics. This unsupervised algorithm learns a tight boundary around normal data points in the feature space, classifying any observation falling outside that boundary as an anomaly. Because it requires no labeled examples of anomalies, one-class SVM is ideal for scenarios like server monitoring where unusual patterns are rare and undefined. On the AWS Certified AI Practitioner AIF-C01 exam, this question tests your ability to match algorithms to unsupervised use cases—a common trap is confusing one-class SVM with clustering methods like K-means, which group data rather than isolate outliers. Remember the boundary concept: one-class SVM draws a circle around “normal,” and anything outside is flagged. A helpful memory tip is to think of a security guard drawing a chalk line around a crowd—anyone stepping outside is suspicious.
This AIF-C01 practice question tests your understanding of fundamentals of ai and ml. Compare every option against the stated constraints before choosing — the best answer satisfies all requirements, not just the most obvious one. 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.
A company wants to automatically detect anomalies in server metrics. Which algorithm is most appropriate?
XGBoost
Why wrong: XGBoost is a supervised gradient boosting algorithm.
One-class SVM
One-class SVM is commonly used for anomaly detection by learning a boundary around normal data.
Linear SVM
Why wrong: Linear SVM is a supervised classifier, not suited for unsupervised anomaly detection.
K-Means
Why wrong: K-Means groups data into clusters, not specifically for anomaly detection.
Answer choices
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.
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.
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.
Related concept
Read the scenario before looking for a memorised answer.
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.
Common exam traps
Cisco 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.
Detailed technical explanation
One-class SVM works by mapping input data into a high-dimensional feature space using a kernel function (e.g., RBF) and then finding a hyperplane that separates the majority of the data from the origin, maximizing the margin. The algorithm's nu parameter controls the trade-off between the fraction of outliers allowed and the tightness of the boundary, making it sensitive to hyperparameter tuning in real-world server monitoring scenarios where metric distributions shift over time.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
An e-commerce site experiences heavy traffic on Black Friday and near-zero traffic during off-peak weeks. Rather than provisioning permanent large VMs, the team uses auto-scaling groups that add capacity automatically under load and reduce it overnight. Questions like this test whether you understand elasticity, availability zones, and cloud compute scaling patterns.
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: 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.
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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