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 supervised, requiring labelled examples of both normal and anomalous behaviour, which server metrics rarely provide at scale. It tempts because gradient boosting excels at tabular prediction, and would be correct if labelled anomaly records existed for training.
- ✓
One-class SVM
Why this is correct
One-class SVM learns a decision boundary describing normal behaviour from only normal samples, flagging deviations as anomalies. Server metrics rarely contain labelled anomalies, so this unsupervised approach suits the scenario better than supervised classifiers requiring labelled attack examples.
- ✗
Linear SVM
Why it's wrong here
A linear SVM needs labelled classes and assumes a linear boundary, which cannot capture the irregular, sparse clusters typical of anomalies. It tempts because SVMs handle high-dimensional data well, and would suit a labelled binary classification task with separable classes.
- ✗
K-Means
Why it's wrong here
K-Means partitions every point into the nearest of k centroids, so outliers are assigned to a cluster rather than flagged as anomalous. It tempts because clustering is unsupervised, and would be correct for segmenting metrics into groups, not for isolating rare deviations.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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