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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. 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. Prompt injection, data poisoning, model inversion
  • 2. Adversarial example defense
  • 3. Threat modeling for AI lifecycles
- 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%- Risk management for AI
  • 1. Risk mitigation and control strategies
  • 2. AI risk identification and assessment
- 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. Organizational AI governance structures
  • 3. Global standards: NIST AI RMF, EU AI Act
AI-assisted Security24%- AI in security strategy and operations
  • 1. Threat modeling and risk assessment
  • 2. Compliance monitoring and auditing
- AI for threat detection and response
  • 1. Anomaly detection and behavioral analysis
  • 2. Automated incident triage and correlation
  • 3. Accelerated threat hunting
- 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 (Q33-Q38):

NEW QUESTION # 33
Which of the following risks most often occurs when developing an internal chatbot?

Answer: C

Explanation:
Data leakage is the most applicable risk because an internal chatbot commonly interacts with organizational information that may include confidential documents, customer information, employee records, credentials, intellectual property, and other sensitive content. CompTIA SecAI+ specifically identifies accidental data leakage as an AI risk and sensitive information disclosure as an attack or failure condition affecting AI systems. Internal chatbots can expose information when retrieval permissions are too broad, conversation context crosses security boundaries, prompts contain confidential material, or model responses reproduce information that the requesting user should not receive. Prompt injection is an important chatbot attack vector, but it describes deliberate manipulation of model instructions rather than the broader organizational risk emphasized here. Model theft concerns unauthorized acquisition or reproduction of a model. Unauthorized access concerns gaining access to resources without proper authorization and is generally addressed through model, data, agent, network, and API access controls. In contrast, information leakage can occur even when legitimate employees are using the chatbot normally. Protecting sensitive data through access controls, minimization, masking, classification, and monitoring is therefore fundamental to internal chatbot security.


NEW QUESTION # 34
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: C

Explanation:
Basic Concept: LLMs process and generate text as discrete units called tokens, which represent words or word pieces. The number of tokens consumed is the primary metric for LLM resource utilization and billing.
Understanding token consumption is essential for both performance monitoring and cost management.
CompTIA SecAI+ Study Guide covers token-based measurement under AI system management.
Why A is Correct: Tokens are the fundamental unit of measurement for LLM utilization. Both input tokens sent in prompts and output tokens generated in responses are counted. Token consumption directly correlates with computational resource usage, response time, and API cost. Monitoring token counts enables the company to establish usage baselines and detect the abnormal token consumption spikes associated with DoS attacks or denial-of-wallet attacks against the LLM.
Why B is Wrong: A transformer is the neural network architecture that underlies modern LLMs. It is an architectural description, not a measurable utilization metric. You cannot quantify LLM resource consumption in " transformers. " Why C is Wrong: Chain of thought is a prompting technique that encourages models to reason step-by-step before providing answers. It is an inference strategy for improving response quality, not a unit of measurement for monitoring LLM utilization or resource consumption.
Why D is Wrong: A prompt is the input text submitted to the LLM. While monitoring prompt volumes provides request count information, tokens provide a more granular and meaningful utilization metric because they capture both the complexity of inputs and the length of generated outputs.


NEW QUESTION # 35
An analyst finds failed login attempts across multiple systems using different usernames but from the same IP. Which attack is MOST likely?

Answer: B

Explanation:
Password spraying uses common passwords across many usernames.


NEW QUESTION # 36
A company wants to reduce IDS false positives. What tuning should occur FIRST?

Answer: C

Explanation:
A behavioral baseline enables effective tuning and alert reduction.


NEW QUESTION # 37
A security operations center (SOC) analyst needs to automate multiple security tasks by breaking them down into smaller parts.
Which of the following AI tools is the best for this task?

Answer: C

Explanation:
Basic Concept: Modern security operations require automation of complex, multi-step workflows. Different AI architectures have different capabilities. Understanding which AI type is best suited for task decomposition and autonomous execution is fundamental to AI-assisted security operations. CompTIA SecAI+ covers agentic AI capabilities under AI-assisted security.
Why A is Correct: Agentic AI systems are specifically designed to autonomously plan, decompose complex tasks into subtasks, execute multi-step workflows, use tools and APIs, and adapt their approach based on intermediate results. For a SOC analyst needing to automate multiple security tasks as a series of smaller coordinated steps, agentic AI is the ideal architecture as it can orchestrate an entire workflow including threat hunting, alert investigation, log analysis, and response actions.
Why B is Wrong: RAG AI enhances language model responses by retrieving relevant documents from a knowledge base. While useful for answering questions with current information, it is not designed for autonomous multi-step task execution or workflow automation.
Why C is Wrong: Generative AI creates content based on prompts including text, code, and summaries. While it can assist with individual tasks, it requires continuous human prompting for each step rather than autonomously breaking down and executing complex multi-step security workflows.
Why D is Wrong: A chatbot is a conversational interface designed for question-answering or guided dialogue.
It responds reactively to user input rather than proactively planning and executing multi-step automated security workflows.


NEW QUESTION # 38
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