AI0-001 Implementing AI Solutions Practice Question
An AI system for detecting anomalies in manufacturing sensor data uses a model trained on normal operation data only. During monitoring, the model flags many false positives. Which adjustment is MOST likely to reduce false positives?
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
✓
Adjust the anomaly detection threshold to be less sensitive (e.g., require a higher reconstruction error)
Changing the anomaly detection threshold (e.g., lowering sensitivity) reduces false positives. Retraining with labeled anomalies is ideal but not always feasible. Using a different model type may not directly reduce false positives.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Switch from an autoencoder to a one-class SVM
Why it's wrong here
Model change may not inherently reduce false positives; threshold tuning is still needed.
- ✗
Add synthetic anomalies to the training set and retrain as a supervised classifier
Why it's wrong here
Adding synthetic anomalies to the training set and retraining as a supervised classifier would convert the problem into a binary classification task, but the stem specifies the model was trained solely on normal operation data. The fundamental mismatch is that the current model has no labelled anomaly examples to learn from, so retraining with synthetic data would require the synthetic anomalies to perfectly represent all real-world fault distributions—an assumption that rarely holds in manufacturing, where novel or rare anomalies are common. This option is tempting because supervised classifiers generally achieve higher precision when both classes are well-represented, and it would be the correct choice if the original training set already contained a representative sample of real anomalies.
- ✓
Adjust the anomaly detection threshold to be less sensitive (e.g., require a higher reconstruction error)
Why this is correct
Raising the threshold means only more extreme deviations are flagged, reducing false positives.
- ✗
Increase the size of the training dataset with more normal operation data
Why it's wrong here
More normal data may improve the model but does not directly lower the false positive rate; threshold adjustment is more direct.
About these practice questions
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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.