AI0-001 AI Security, Ethics and Governance Practice Question
You are a security engineer at a large e-commerce company that uses an AI-based recommendation system. The system is deployed on a Kubernetes cluster and uses a TensorFlow model served via REST API. Recently, the security team detected unusual API calls that caused the model to return incorrect recommendations. Analysis shows that the inputs were crafted to maximize prediction error. The team suspects an adversarial attack. You need to implement a solution that detects and mitigates such attacks in real-time without requiring model retraining. Which approach should you take?
⚠ Common exam trap
CompTIA often tests the misconception that retraining or scaling can solve security issues, but the key constraint here is 'real-time detection without retraining,' which eliminates options that require model modification or do not address the attack vector.
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
✓
Implement an input validation filter to detect and block anomalous inputs
An input validation filter can detect and block adversarial inputs in real-time by analyzing statistical properties (e.g., outlier detection, perturbation magnitude) without modifying the model. This approach is lightweight, operates at the API gateway level, and does not require retraining, making it suitable for immediate deployment against crafted inputs that maximize prediction error.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Implement an input validation filter to detect and block anomalous inputs
Why this is correct
An input validation filter inspects incoming REST payloads against learned feature distributions, flagging perturbations that maximise prediction error before inference. This satisfies the real-time constraint without retraining, since detection operates on the input manifold rather than model weights. Blocking anomalous vectors prevents crafted adversarial examples from reaching the TensorFlow serving endpoint.
- ✗
Increase the number of model replicas to distribute the load
Why it's wrong here
Adding replicas scales throughput and availability; each replica still returns the same perturbed predictions for crafted inputs, so no detection or mitigation happens. It is tempting for handling API load, but the requirement is real-time adversarial input detection without retraining, which replication does not provide.
- ✗
Retrain the model with adversarial examples
Why it's wrong here
Retraining bakes adversarial robustness into the model offline, so it cannot inspect live REST traffic or block crafted inputs as they arrive; the stem explicitly excludes retraining. It is tempting because adversarial training genuinely hardens models, and would be the right choice when you control the training pipeline and can accept the cost of regenerating the model.
- ✗
Roll back the model to a previous version that was not attacked
Why it's wrong here
Rolling back restores the previous weights, yet the crafted inputs still maximise prediction error against that model, so detection and mitigation do not occur in real time. It is tempting as incident response after compromise, but it addresses model integrity, not adversarial input filtering at inference.
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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.