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AI0-001 AI Security Practice Question

A developer is building an AI-powered code completion tool. They want to ensure that the tool does not inadvertently suggest insecure code patterns. Which practice is MOST effective for reducing this risk?

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

Red teaming the AI system

Red teaming involves adversarial testing to find vulnerabilities. Output filtering can catch some insecure suggestions but may not cover all patterns. Secure data pipelines focus on training data security, not output. Rate limiting is unrelated.

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

  • Red teaming the AI system

    Why this is correct

    Red teaming proactively probes the model for harmful outputs, including insecure code suggestions, allowing fixes before deployment.

  • Rate limiting

    Why it's wrong here

    Rate limiting controls request frequency but does not prevent insecure suggestions.

  • Secure data pipelines

    Why it's wrong here

    Secure data pipelines protect training data but do not directly address output safety.

  • Output filtering of insecure patterns

    Why it's wrong here

    Output filtering can block known insecure patterns but may miss novel ones; red teaming is more comprehensive.

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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.