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)
Isolation Forest with sliding windows is a well-suited unsupervised technique for detecting anomalies in time-series data by isolating outliers in feature windows extracted from the log stream. Option C is correct because the company has labeled data ('normal' and 'anomaly'), enabling a supervised classification model like XGBoost to learn patterns from engineered features and predict anomalies accurately.
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
If labeled data is available, supervised models can effectively detect anomalies.
- ✗
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.
About these practice questions
This AI0-001 question is part of Courseiva's 754-question bank — original exam-style content with full explanations and wrong-answer analysis, never real exam questions or exam dumps. Learn why practice questions differ from exam dumps →
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.