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CompTIA CY0-001 Exam Syllabus Topics:

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

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Hot CY0-001 Valid Test Test | High Pass-Rate CompTIA CY0-001 Valid Braindumps Book: CompTIA SecAI+ Certification Exam

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CompTIA SecAI+ Certification Exam Sample Questions (Q14-Q19):

NEW QUESTION # 14
Which of the following controls is the best way to mitigate a denial-of-service (DoS) attack?

Answer: D

Explanation:
Rate limiting restricts the number of requests within a set timeframe, preventing attackers from overwhelming the system with excessive traffic, making it the best control to mitigate a DoS attack.


NEW QUESTION # 15
SIMULATION
Instructions
Click the (+) to assign each threat category into its appropriate framework.
If at any time you would like to bring back the initial state of the simulation, please click the Reset All button.
An architect is modeling an agentic system to meet security standards.

Answer:

Explanation:

Explanation:
MAESTRO: Overreliance, Insecure plug-in design
OWASP Top 10: Broken access control, Identification and authentication failures OWASP Top 10 LLM: Prompt injection, Model denial of service STRIDE: Elevation of privilege, Repudiation, Supply chain vulnerabilities, Insecure design MAESTRO addresses AI-specific misuse such as excessive trust in outputs (overreliance) and unsafe plug-in design.
OWASP Top 10 maps to classic web threats: broken access control and authentication failures are key risks.
OWASP Top 10 LLM focuses on LLM-related threats like prompt injection and denial of service targeting the model.
STRIDE categories cover privilege escalation, repudiation (denying actions), supply chain risks, and insecure design flaws.


NEW QUESTION # 16
A company introduces a large language model (LLM) in an application in order to monitor for a potential denial-of-service attack. Which of the following should the company use to measure the utilization of the LLM?

Answer: B

Explanation:
Tokens are the most appropriate measurement because LLM processing and resource consumption are fundamentally tied to the number of tokens processed in prompts and generated in responses. CompTIA SecAI+ specifically includes token limits among gateway security controls and requires knowledge of AI monitoring for prompts, responses, processing, and cost.
Monitoring token consumption is particularly relevant when investigating denial-of-service or resource- exhaustion conditions. An attacker can submit excessive, repeated, or unusually large inputs that consume the model ' s context capacity and computational resources. Tracking token utilization therefore gives administrators a practical indicator of how heavily the LLM is being consumed and can support thresholds, quotas, rate controls, and anomaly detection.
A transformer is the neural-network architecture underlying many modern LLMs; it is not a unit used to measure utilization. A chain of thoughts refers to intermediate reasoning behavior and does not quantify model consumption. A prompt is an input submitted to the model, but simply counting prompts does not account for their potentially very different sizes. Token measurement therefore provides the most useful utilization metric.


NEW QUESTION # 17
An organization recently created a custom model that integrates with a language model (LLM).
The developer notices that the application programming interface (API) costs have increased.
Which of the following is the best control to reduce cost?

Answer: A

Explanation:
API costs for large language model integrations are directly tied to token usage (input + output tokens). By adjusting token limits, the organization can reduce unnecessary processing of overly long prompts or responses, thereby lowering overall API costs without changing model size or infrastructure resources.


NEW QUESTION # 18
A security architect performs threat modeling of an AI system. The architect needs to determine which attacks can be performed against the system.
Which of the following actions should the architect take next?

Answer: C

Explanation:
MITRE ATLAS is specifically designed to catalog adversarial TTPs targeting AI systems. By analyzing ATLAS, the architect can determine which types of attacks are possible against the AI system, making it the most appropriate resource for threat modeling in this context.


NEW QUESTION # 19
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