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CompTIA CY0-001 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: AI-assisted Security24%- Security automation and orchestration
  • 1. Vulnerability management and assessment
  • 2. Workflow automation and response playbooks
- AI in security strategy and operations
  • 1. Compliance monitoring and auditing
  • 2. Threat modeling and risk assessment
- AI for threat detection and response
  • 1. Automated incident triage and correlation
  • 2. Accelerated threat hunting
  • 3. Anomaly detection and behavioral analysis
Topic 2: AI Governance, Risk and Compliance19%- Governance frameworks and policies
  • 1. Responsible AI principles and ethics
  • 2. Organizational AI governance structures
  • 3. Global standards: NIST AI RMF, EU AI Act
- Risk management for AI
  • 1. AI risk identification and assessment
  • 2. Risk mitigation and control strategies
- Compliance and legal requirements
  • 1. Data protection and privacy laws
  • 2. Transparency, accountability and auditability
Topic 3: Basic AI Concepts Related to Cybersecurity17%- AI-driven threats and risks
  • 1. Malicious use of generative AI
  • 2. Automated phishing, polymorphic malware
  • 3. Adversarial machine learning attacks
- Core AI principles and terminology
  • 1. Generative AI concepts and capabilities
  • 2. Machine learning, deep learning, NLP, automation
- AI applications in security
  • 1. Threat detection and anomaly analysis
  • 2. Security automation and decision support
Topic 4: Securing AI Systems40%- Security controls for AI systems
  • 1. Data protection: integrity, confidentiality, privacy
  • 2. Deployment environment security
  • 3. Model security: access, integrity, anti-tampering
- Secure AI development and operations
  • 1. DevSecOps integration for AI
  • 2. Secure MLOps and AI pipeline design
- Defending against AI-specific attacks
  • 1. Adversarial example defense
  • 2. Threat modeling for AI lifecycles
  • 3. Prompt injection, data poisoning, model inversion

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CompTIA SecAI+ Certification Exam Sample Questions (Q155-Q160):

NEW QUESTION # 155
During the selection of a machine learning (ML)-based threat classification model, a cybersecurity administrator verifies that label distribution is highly unbalanced. Which of the following processing techniques should the engineer use to balance the model?

Answer: C

Explanation:
When label distribution is highly unbalanced, data augmentation generates additional synthetic samples for the underrepresented classes. This balances the dataset, improving the ML model's ability to classify threats accurately across all categories.


NEW QUESTION # 156
A security consultant needs to detect attacks across a large language model (LLM) firewall.
Which of the following techniques should the consultant use?

Answer: B

Explanation:
Basic Concept: LLM firewalls inspect prompts and responses to identify malicious content, policy violations, and attack attempts. To detect known attack patterns, these systems apply inspection techniques that compare content against established threat indicators. CompTIA SecAI+ Study Guide covers LLM security monitoring and detection techniques.
Why A is Correct: Signature matching compares incoming prompts and outgoing responses against a library of known attack signatures, including common prompt injection patterns, jailbreaking attempts, data exfiltration queries, and known malicious payload strings. When content matches a known attack signature, the LLM firewall can block or flag it. Signature matching is an efficient, proven detection technique for identifying known attack patterns traversing an LLM firewall.
Why B is Wrong: Distributed denial-of-service is itself a type of attack, not a detection technique. DDoS floods systems with traffic to cause service unavailability and has no role in detecting attacks through an LLM firewall.
Why C is Wrong: Translation analysis involves converting content between languages or formats. While it might be used to detect obfuscated attacks in different encodings, it is not a standard detection technique for identifying attacks crossing an LLM firewall.
Why D is Wrong: Vulnerability enumeration systematically identifies and catalogs vulnerabilities in systems or applications during security assessments. It is an assessment activity used to discover weaknesses, not a real-time detection technique for attacks traversing an LLM firewall.


NEW QUESTION # 157
Which of the following certifies an organization in the use of responsible AI?

Answer: B

Explanation:
ISO/IEC 42001 is the correct answer because it is an Artificial Intelligence Management System (AIMS) standard that organizations can implement to demonstrate structured and responsible governance of AI.
CompTIA SecAI+ explicitly includes ISO AI standards within the governance, risk, and compliance domain and expects candidates to distinguish standards from regulatory frameworks. ISO/IEC 42001 specifies requirements for establishing, implementing, maintaining, and continually improving an AI management system. Its scope includes responsible AI use, governance, risk management, accountability, transparency, and organizational controls. ISO describes the standard as applicable to organizations that develop, provide, or use AI systems and specifically identifies responsible AI and AI governance among its benefits. GDPR is legally binding privacy legislation rather than an AI management-system certification. The EU AI Act is also legislation and establishes regulatory requirements based on AI risk. NIST AI RMF provides voluntary risk- management guidance but is not itself an organizational certification standard. Therefore, ISO/IEC 42001 is the option specifically designed to support certifiable organizational AI-management practices.


NEW QUESTION # 158
An AI security administrator notices that the information referenced by the model is incorrectly formatted and missing values.
Which of the following job roles would most likely be responsible for correcting this error?

Answer: C

Explanation:
Basic Concept: In AI teams, different roles hold distinct responsibilities for specific aspects of the AI system.
Data quality issues such as incorrect formatting and missing values fall within the domain of data engineering, which is responsible for designing, building, and maintaining the data pipelines and datasets that feed AI models. CompTIA SecAI+ Study Guide covers AI team role definitions under governance and basic AI concepts.
Why C is Correct: A Data Engineer is responsible for building and maintaining the data pipelines, transformation processes, and data quality controls that supply AI models with properly formatted, complete data. Addressing incorrectly formatted data and missing values is a core data engineering responsibility, as these professionals own the ETL (Extract, Transform, Load) processes, data validation rules, and data quality frameworks that ensure the model receives clean, usable input.
Why A is Wrong: A Platform Engineer designs and maintains the computing infrastructure and platforms that host AI systems. Their focus is the technical environment and deployment infrastructure, not the quality or formatting of the data consumed by models.
Why B is Wrong: An MLOps Engineer manages the deployment, monitoring, and operational lifecycle of AI models in production. While they may detect data quality issues through monitoring, resolving data formatting and missing value problems is the responsibility of data engineers who own the data pipelines.
Why D is Wrong: An AI Architect designs the overall AI system architecture, component interactions, and technical strategy. While they define data requirements, the hands-on work of correcting data formatting errors and filling missing values belongs to the data engineering function.


NEW QUESTION # 159
A short AI-generated video shows a celebrity's likeness talking about a fake public security event.
Which of the following was used to create this video?

Answer: B

Explanation:
Convolutional neural networks (CNNs) are commonly used in generating deepfake videos, where a person's likeness is realistically mapped and animated to create fake but convincing audiovisual content.


NEW QUESTION # 160
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