CY0-001的中合格問題集 & CY0-001試験情報

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CompTIA CY0-001 Exam Syllabus Topics:

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

>> CY0-001的中合格問題集 <<

CY0-001試験情報、CY0-001日本語関連対策

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CompTIA SecAI+ Certification Exam 認定 CY0-001 試験問題 (Q151-Q156):

質問 # 151
What control reduces the impact radius when a single host is compromised?

正解：D

解説：
Segmentation isolates systems and limits lateral movement.


質問 # 152
A group of security engineers is developing a SIEM system that will be able to ingest data from multiple structured and unstructured sources, have a chatbot integrated with an LLM that the security analyst can interact with, and provide insights from the SIEM alert data.
Which of the following techniques should the security engineers consider before collecting the data from the respective sources?

正解：A

解説：
Basic Concept: Before ingesting data from multiple sources - both structured and unstructured - into an AI- powered SIEM system, the data must be prepared to ensure quality, consistency, and usability. Data from diverse sources often contains noise, errors, duplicates, and formatting inconsistencies that will degrade AI performance if not addressed. CompTIA SecAI+ covers data preparation as a prerequisite for AI system effectiveness.
Why C is Correct: Data cleansing is the process of detecting and correcting or removing corrupt, inaccurate, incomplete, and duplicate data. Before collecting data from multiple structured and unstructured SIEM sources, engineers must cleanse the data to standardize formats, remove duplicates, fill missing values, and eliminate noise. Clean input data is fundamental to producing accurate AI-generated insights and reliable LLM interactions in the SIEM context.
Why A is Wrong: Balancing addresses class distribution imbalance in labeled training data for classification models. While relevant when training ML detection models, it is not the primary consideration before initial data collection from diverse SIEM sources.
Why B is Wrong: Verification confirms that data meets expected quality standards and validates its accuracy against trusted sources. It is a post-collection quality check performed after cleansing, not the first step before data collection.
Why D is Wrong: Vector storage refers to databases that store embeddings for semantic search, relevant for RAG systems. It is a storage architecture decision made after data is collected, processed, and prepared, not a pre-collection technique.


質問 # 153
Instructions: Click the (+) to assign each threat category into its appropriate framework.
An architect is modeling an agentic system to meet security standards.

正解：

解説：
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.


質問 # 154
A company launches an AI application to monitor cloud misconfiguration and compliance. The AI application is shutting down development servers and opening ports during a client demonstration. Which of the following actions should the company take to return to normal operations and prevent future issues?

正解：A


質問 # 155
A financial organization implements a new AI-based fraud detection system to flag suspicious transactions. A security analyst discovers that it occasionally blocks legitimate transactions.
Which of the following is the best recommendation?

正解：B

解説：
False positives occur when the AI model lacks sufficient or representative training data.
Retraining the model with more diverse and recent transaction patterns improves accuracy, reducing the chance of legitimate transactions being incorrectly flagged.


質問 # 156
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