CY0-001 Latest Practice Materials, CY0-001 Sample Exam

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CompTIA CY0-001 Exam Syllabus Topics:

SectionWeightObjectives
AI Governance, Risk and Compliance19%- Compliance and legal requirements
  • 1. Transparency, accountability and auditability
  • 2. Data protection and privacy laws
- Governance frameworks and policies
  • 1. Organizational AI governance structures
  • 2. Responsible AI principles and ethics
  • 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
Securing AI Systems40%- Security controls for AI systems
  • 1. Deployment environment security
  • 2. Data protection: integrity, confidentiality, privacy
  • 3. Model security: access, integrity, anti-tampering
- Defending against AI-specific attacks
  • 1. Prompt injection, data poisoning, model inversion
  • 2. Threat modeling for AI lifecycles
  • 3. Adversarial example defense
- Secure AI development and operations
  • 1. Secure MLOps and AI pipeline design
  • 2. DevSecOps integration for AI
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-driven threats and risks
  • 1. Malicious use of generative AI
  • 2. Automated phishing, polymorphic malware
  • 3. Adversarial machine learning attacks
- AI applications in security
  • 1. Security automation and decision support
  • 2. Threat detection and anomaly analysis
AI-assisted Security24%- AI for threat detection and response
  • 1. Anomaly detection and behavioral analysis
  • 2. Accelerated threat hunting
  • 3. Automated incident triage and correlation
- 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

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CompTIA SecAI+ Certification Exam Sample Questions (Q118-Q123):

NEW QUESTION # 118
A security team is using an AI-based tool to try to bypass organizational boundaries. The team uses AI to look at the current state and suggest different attack vectors based on the outcome of the previous ones. Which of the following techniques is the team most likely using?

Answer: A

Explanation:
The described behavior - iteratively assessing the environment and adapting attack paths based on prior outcomes - matches automated penetration testing, where AI-driven tools simulate attack chains and adjust tactics dynamically.


NEW QUESTION # 119
Hotspot Question
Instructions
Use the drop-down menus to define two appropriate security controls for each component of the AI system. Each control may be used only once.
If at any time you would like to bring back the initial state of the simulation please click the Reset All button.
An engineer is deploying a new AI system and wants to integrate it into the core system through an API.

Answer:

Explanation:

Explanation:
Cloud control plane: IAM policies, Resource policies
Prompt firewall: Injection policies, Output monitoring
WAF: Connection rate limits, Input token validation
Front-end API + Model + Vector database: Guardrails, Input quota
API gateway: Load balancing, Authentication token validation
Cloud control plane relies on IAM and resource policies to enforce centralized identity and access management as well as usage restrictions.
Prompt firewall protects against malicious input via injection policies and ensures outputs are monitored for compliance and safety.
WAF controls connection rate to mitigate DoS and validates input tokens to stop unauthorized access attempts.
Front-end API, Model, and Vector database share guardrails to restrict unsafe behaviors and input quotas to prevent overuse, making them consistent and efficient security points.
API gateway enforces load balancing for availability and validates authentication tokens at the entry point, providing secure and scalable access.


NEW QUESTION # 120
A data set containing medical information is put into a machine learning (ML) model that is designed to predict specific illnesses for a population. In the process of verifying the reliability of the system, the compliance officer realizes that the system cannot reliably predict illnesses for certain segments of the population. Which of the following types of risk is most applicable to this case?

Answer: B

Explanation:
The model's inability to reliably predict illnesses for certain population segments indicates bias in the dataset or training process. This leads to unfair or inaccurate outcomes for specific groups, making bias the most applicable risk in this case.


NEW QUESTION # 121
A cybersecurity analyst must use pattern recognition on a data set containing unstructured data.
Which of the following models is the best for this task?

Answer: D

Explanation:
Convolutional neural networks (CNNs) are highly effective at detecting patterns in unstructured data such as images, audio, or text embeddings. Their ability to automatically learn spatial or hierarchical features makes them the best choice for pattern recognition on unstructured datasets.


NEW QUESTION # 122
A cybersecurity analyst wants to choose a machine learning (ML) model to classify log entries while providing the best explainability. Which of the following models should the analyst use?

Answer: B

Explanation:
Decision trees provide clear and interpretable decision paths, making them highly explainable compared to complex models like neural networks or GANs. This makes them the best choice for classifying log entries when explainability is a priority.


NEW QUESTION # 123
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