CY0-001–100% Free Sample Questions Pdf | Accurate Reliable CompTIA SecAI+ Certification Exam Study Materials

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CompTIA CY0-001 Exam Syllabus Topics:

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

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CompTIA SecAI+ Certification Exam Sample Questions (Q23-Q28):

NEW QUESTION # 23
An internal user enters a client credit card number into an internal generative machine learning (ML) model:
#User prompt: Customer Jane Doe has a new credit card that she wants to add to her account. The number is 5555-5555-5555-5555 Which of the following is the most effective way to prevent prompt injection attacks against a large language model (LLM)?

Answer: C

Explanation:
Guardrails are the primary security control for LLMs to prevent prompt injection attacks. They enforce rules on what inputs are accepted and how the model responds, blocking malicious or sensitive prompts (such as credit card numbers) before they can manipulate or exploit the model.


NEW QUESTION # 24
A social media company with more than a million lines of code wants to reduce the mean time to fix bugs and issues. Which of the following is the most balanced AI strategy to automate the vulnerability management flow?

Answer: A

Explanation:
This approach balances automation and human oversight. AI accelerates vulnerability management by triaging issues and generating tickets, while software engineers retain responsibility for merging code changes, ensuring quality and reducing the risk of insecure or unstable code being deployed.


NEW QUESTION # 25
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?

Answer: C

Explanation:
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.


NEW QUESTION # 26
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 # 27
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: A

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 # 28
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