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CompTIA CY0-001 Exam Syllabus Topics:

Section	Weight	Objectives
AI-assisted Security	24%	- AI in security strategy and operations 1. Threat modeling and risk assessment 2. Compliance monitoring and auditing - AI for threat detection and response 1. Automated incident triage and correlation 2. Anomaly detection and behavioral analysis 3. Accelerated threat hunting - Security automation and orchestration 1. Workflow automation and response playbooks 2. Vulnerability management and assessment
Basic AI Concepts Related to Cybersecurity	17%	- Core AI principles and terminology 1. Generative AI concepts and capabilities 2. Machine learning, deep learning, NLP, automation - AI-driven threats and risks 1. Adversarial machine learning attacks 2. Malicious use of generative AI 3. Automated phishing, polymorphic malware - AI applications in security 1. Threat detection and anomaly analysis 2. Security automation and decision support
AI Governance, Risk and Compliance	19%	- Compliance and legal requirements 1. Transparency, accountability and auditability 2. Data protection and privacy laws - Risk management for AI 1. AI risk identification and assessment 2. Risk mitigation and control strategies - Governance frameworks and policies 1. Organizational AI governance structures 2. Responsible AI principles and ethics 3. Global standards: NIST AI RMF, EU AI Act
Securing AI Systems	40%	- Defending against AI-specific attacks 1. Prompt injection, data poisoning, model inversion 2. Threat modeling for AI lifecycles 3. Adversarial example defense - Security controls for AI systems 1. Deployment environment security 2. Model security: access, integrity, anti-tampering 3. Data protection: integrity, confidentiality, privacy - Secure AI development and operations 1. DevSecOps integration for AI 2. Secure MLOps and AI pipeline design

CompTIA SecAI+ Certification Sample Questions:

1. Which of the following explains the reason a cybersecurity analyst prefers a machine learning (ML) model over a statistical model for attack classification?

A) A simplified development pipeline and deployment process
B) Large community support and availability of global experts
C) The ability to learn complex problems and adapt to new information
D) Improved performance with a small data set and high durability

2. Which of the following job roles in an organizational governance structure develops a model from business use cases?

A) Platform architect
B) Data scientist
C) AI risk analyst
D) Machine learning operations (MLOps) engineer

3. A company deploys an internet-facing chatbot using RAG. Logs show that an administrator can retrieve employee names and usernames while an employee receives ' information not available. ' Which of the following is reducing the risk of sensitive data exposure in this scenario?

A) Data access controls
B) Model-specific guardrails
C) Rate limiting
D) Prompt templates

4. A recently deployed AI system becomes persistently unavailable. A restart temporarily fixes the issue, but the issue happens again. Upon examination of API logs, an analyst finds that external calls continued to use system resources after the action completed.
Which of the following is the best way to improve availability of the system?

A) Increasing system memory
B) Enforcing session expiration
C) Implementing multifactor authentication (MFA)
D) Creating token limits

5. Which of the following is a risk addressed by responsible AI?

A) Reputational loss
B) Response bias
C) Data poisoning
D) Model drift

Solutions:

Question # 1
Answer: C

Question # 2
Answer: B

Question # 3
Answer: A

Question # 4
Answer: B

Question # 5
Answer: B