CY0-001퍼펙트공부문제 - CY0-001적중율높은덤프공부

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CompTIA CY0-001 Exam Syllabus Topics:

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

>> CY0-001퍼펙트 공부문제 <<

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최신 CompTIA SecAI+ CY0-001 무료샘플문제 (Q75-Q80):

질문 # 75
Instructions: Click the (+) to assign each threat category into its appropriate framework.
An architect is modeling an agentic system to meet security standards.

정답：

설명：
See Explanation below for complete solution for this PBQ.
Explanation:

Basic Concept: This is a Performance-Based Question (PBQ) - a simulation item requiring interactive drag- and-drop assignment of threat categories to appropriate frameworks in the actual exam. It tests knowledge of how different AI threat frameworks categorize and address specific threat types for agentic systems.
Key Concept - Framework-to-Threat Mapping: MITRE ATLAS covers ML-specific adversarial tactics such as model evasion, data poisoning, model extraction, and prompt injection for agentic systems. OWASP LLM Top 10 addresses application-level LLM vulnerabilities such as insecure output handling, excessive agency, and supply chain risks. NIST AI RMF addresses governance-level risks across the AI lifecycle. STRIDE addresses architectural threats including spoofing, tampering, repudiation, information disclosure, DoS, and elevation of privilege.
Why This Matters: Agentic AI systems have a unique threat landscape combining traditional software vulnerabilities with AI-specific attacks. Correctly mapping threat categories to frameworks is essential for comprehensive threat modeling of systems that autonomously execute multi-step tasks with tool access and real-world consequences.
Reference: CompTIA SecAI+ Study Guide Domain 4 covers AI governance frameworks and their specific threat categories. Candidates should understand the scope and focus areas of MITRE ATLAS, OWASP LLM Top 10, NIST AI RMF, and traditional security frameworks as they apply to agentic AI system security modeling.


질문 # 76
A security analyst receives an alert about an AI system and is investigating the following output:

Which of the following is the most appropriate control the analyst should recommend?

정답：D

설명：
Basic Concept: Suspicious or unexpected AI system outputs are often caused by malicious or malformed user inputs that exploit the AI ' s input processing. Validating and sanitizing user inputs before they reach the AI model prevents many classes of attacks including prompt injection, data exfiltration attempts, and input manipulation. CompTIA SecAI+ Study Guide emphasizes input validation as a foundational AI security control.
Why B is Correct: Implementing user input validation applies checks and constraints to all user-submitted content before it is processed by the AI system. Input validation can enforce length limits, detect injection patterns, filter disallowed characters or command structures, and ensure inputs conform to expected formats.
By rejecting malicious or malformed inputs at the entry point, this control prevents them from reaching the model and causing the suspicious outputs observed.
Why A is Wrong: Data sanitization processes data to remove harmful elements and is closely related to validation. However, input validation is broader and more proactive, checking conformance to rules before processing, while sanitization typically operates during or after processing. Validation at the input boundary is the more appropriate first-line control.
Why C is Wrong: Monitoring logs for attack keywords is a detective control that identifies attacks after they have already affected the system. It does not prevent suspicious outputs from being generated in the first place.
Why D is Wrong: Hardening the Model Context Protocol server improves the security of the infrastructure hosting the AI components. While important for infrastructure security, it does not directly validate or inspect the content of user inputs that cause suspicious outputs.


질문 # 77
An administrator, who works for a financial institution, is required to implement data security controls for data at rest within AI systems that involve data disclosure. Which of the following is the most suitable control?

정답：D

설명：
For financial institutions handling AI systems, protecting data at rest against disclosure requires encryption. Encryption ensures that even if the storage medium is accessed or compromised, the data remains unreadable without the proper decryption keys.


질문 # 78
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?

정답：D

설명：
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.


질문 # 79
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?

정답：D

설명：
A data engineer is responsible for preparing, cleaning, and formatting data pipelines. When information is incorrectly formatted or missing values, the data engineer ensures data integrity and quality before it is used by AI models.


질문 # 80
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