AI0-001 Implementing AI Solutions Practice Question
A company wants to use AI to automatically detect anomalies in server log data. The data is time-series and labeled with 'normal' and 'anomaly' for the past year. Which TWO techniques are appropriate for this use case?
⚠ Common exam trap
The AI0-001 exam often tests the distinction between supervised and unsupervised techniques, and candidates mistakenly choose an unsupervised method (like Isolation Forest) when labeled data is available, or they overlook that both supervised and unsupervised approaches can be valid depending on the data and problem framing.
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
✓
Use a time-series anomaly detection model (e.g., Isolation Forest with sliding windows)
Option B is correct because the data is time-series log data, and techniques like Isolation Forest applied over sliding windows (or similar time-series anomaly detectors) are designed to capture temporal patterns and flag deviations from normal behavior without requiring the labels, which suits anomaly detection on sequential log streams. Option C is correct because the dataset is labeled with 'normal' and 'anomaly' for a full year, so a supervised classifier such as XGBoost can be trained on extracted features (e.g., counts, rates, error codes, latency statistics) to directly learn the mapping from features to the anomaly label. Option A is not appropriate because converting logs to graph screenshots and using a CNN image classifier discards the underlying time-series structure and numeric log semantics, making it an indirect and lossy approach. Option D is wrong because code generation models fix code rather than detect anomalies in log data, which is the stated goal. Option E is wrong because a recommendation system based on user activity logs addresses personalization, not anomaly detection in server logs.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Train an image classification model (CNN) on screenshots of log graphs
Why it's wrong here
Unnecessary; direct log data is structured, not image-based.
- ✓
Use a time-series anomaly detection model (e.g., Isolation Forest with sliding windows)
Why this is correct
Isolation Forest works on numerical features; sliding windows capture temporal patterns.
- ✓
Train a supervised classification model (e.g., XGBoost) on extracted features with the labels
Why this is correct
XGBoost learns a decision boundary from extracted features using the year of normal and anomaly labels, exploiting the supervised signal the stem provides. It handles tabular log-derived features well, though temporal ordering must be encoded explicitly.
- ✗
Use a code generation model to fix the anomalies automatically
Why it's wrong here
Code generation does not detect anomalies; it generates code from prompts.
- ✗
Build a recommendation system based on user activity logs
Why it's wrong here
Recommendation systems serve content; not for anomaly detection.
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JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This AI0-001 practice question is part of Courseiva's free CompTIA 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 AI0-001 exam.