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CompTIA CY0-001 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: AI Governance, Risk, and Compliance	19%	- AI Governance Frameworks 1. ISO/IEC AI governance alignment 2. NIST AI RMF concepts - Risk and Compliance 1. Ethical AI and responsible usage policies 2. Regulatory compliance for AI systems
Topic 2: AI-Assisted Security	24%	- Security Operations Enhancement 1. SOC automation and alert correlation 2. AI-driven threat detection and anomaly detection - Operational Use of AI 1. AI-enabled threat intelligence analysis 2. Incident response acceleration
Topic 3: Securing AI Systems	40%	- Adversarial Defense 1. Data poisoning mitigation 2. Model extraction and inference attack defense - AI System Protection 1. Secure AI pipelines and deployment environments 2. Data protection and model security
Topic 4: Basic AI Concepts Related to Cybersecurity	17%	- AI Threat Landscape 1. AI-driven cyber threats (phishing, malware automation) 2. Adversarial AI and model manipulation - Generative AI Concepts 1. Prompting and AI interaction fundamentals 2. LLMs and generative AI basics - AI and Machine Learning Fundamentals 1. Supervised, unsupervised, reinforcement learning 2. Neural networks and deep learning basics

>> CY0-001퍼펙트 덤프 최신문제 <<

적중율 높은 CY0-001퍼펙트 덤프 최신문제 인증덤프공부

최신 CompTIA SecAI+ CY0-001 무료샘플문제 (Q137-Q142):

질문 # 137
Which of the following attacks would be the best to automate with AI during dynamic application software testing (DAST)?

A. Distributed denial-of-service (DDoS)
B. Payload creation
C. Data poisoning
D. Threat modeling

정답：B

설명：
During DAST, automating the generation of diverse, targeted attack payloads lets testers probe runtime inputs (e.g., XSS, SQLi, command injection) more thoroughly and discover vulnerabilities that manual or static tests might miss.

질문 # 138
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?

A. Chain of thoughts
B. Prompt
C. Token
D. Transformer

정답：C

설명：
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.

질문 # 139
An AI security administrator receives an inquiry about an unusually high monthly bill from the AI solution provider. The administrator thinks the majority of staff might be using the most powerful model available.
Which of the following AI measures should the administrator implement to lower costs?

A. Storage monitoring
B. Modality types
C. Prompt firewalls
D. Token limits

정답：D

설명：
Basic Concept: LLM API billing is primarily based on token consumption. High costs resulting from staff using powerful, verbose models can be controlled by limiting the maximum tokens processed per interaction and restricting which models staff can access. CompTIA SecAI+ Study Guide covers token management as the primary cost control mechanism for AI deployments.
Why D is Correct: Implementing token limits caps the maximum tokens consumed per API call for both input and output. This directly controls the per-interaction cost by preventing excessively long prompts or overly verbose model responses from generating large token bills. Combined with model tier restrictions, token limits ensure that interactions with powerful models remain within budget constraints regardless of how extensively staff use the service.
Why A is Wrong: Storage monitoring tracks the utilization and performance of data storage systems. Storage costs are separate from LLM API token-based billing and monitoring storage does not address the high API charges resulting from excessive model usage by staff.
Why B is Wrong: Modality types refer to the input formats an AI model accepts such as text, images, audio, or video. While different modalities have different pricing, managing modality types is not the direct cost control lever. Token consumption is the primary cost driver for text-based interactions.
Why C is Wrong: Prompt firewalls inspect and filter prompt content for security and policy compliance.
While they can block certain types of queries, they are designed for security purposes, not as financial controls to limit token consumption or enforce cost budgets across staff usage.

질문 # 140
A disgruntled employee changed the company policies that a chatbot references in order to create confusion and disrupt the business.
Which of the following AI-generated vulnerabilities is the employee exploiting?

A. Data poisoning
B. Data leaking
C. Data masking
D. Data reduction

정답：A

설명：
Basic Concept: AI systems that rely on knowledge bases, vector databases, or reference documents are vulnerable to attacks that corrupt or manipulate that source data. When an adversary deliberately modifies the data an AI uses, this is a form of data poisoning. CompTIA SecAI+ Study Guide covers data poisoning as a core AI vulnerability.
Why C is Correct: Data poisoning is an attack where an adversary intentionally corrupts or manipulates the data that an AI system uses for training, inference, or reference. In this scenario, the employee modified the company policies document that the chatbot uses as its knowledge base, causing the chatbot to provide incorrect, misleading, or confusing information to users. This is a classic indirect data poisoning attack targeting the AI ' s reference data rather than its model weights.
Why A is Wrong: Data reduction refers to techniques that decrease the volume or dimensionality of data for processing efficiency. It is a data engineering concept, not an attack vector or vulnerability classification.
Why B is Wrong: Data masking replaces sensitive data values with anonymized equivalents to protect privacy. It is a data protection control used legitimately, not an attack that an employee would exploit to cause disruption.
Why D is Wrong: Data leaking involves unauthorized disclosure of sensitive information from an AI system or its associated data stores. The employee ' s action of manipulating data is an integrity attack, not a confidentiality violation involving leakage of data to unauthorized parties.

질문 # 141
Which of the following should an auditor reference when reviewing a company ' s human resources AI systems for legal non-compliance?

A. European Union (EU) AI Act
B. National Institute of Standards and Technology (NIST) AI Risk Management Framework (RMF)
C. Organization for Economic Cooperation and Development (OECD) standard
D. International Organization for Standardization (ISO)

정답：A

설명：
Basic Concept: Various regulatory frameworks govern AI use in different contexts. For auditing legal compliance in high-risk AI applications such as employment and HR, binding regulatory legislation takes precedence over voluntary standards. CompTIA SecAI+ Exam Objectives cover AI governance and compliance frameworks under Domain 4.
Why C is Correct: The EU AI Act is the world ' s first comprehensive, legally binding AI regulation. It explicitly classifies AI systems used in employment, worker management, and recruitment as high-risk AI systems, subjecting them to strict compliance requirements including conformity assessments, transparency obligations, and human oversight mandates. An auditor reviewing HR AI for legal non-compliance must reference this binding legislation.
Why A is Wrong: The OECD AI Principles are non-binding international guidelines promoting responsible AI. They offer policy guidance but carry no legal enforcement power for compliance auditing.
Why B is Wrong: The NIST AI RMF is a voluntary, risk management-focused framework. It is not a legal compliance standard and cannot be used to assess legal non-compliance.
Why D is Wrong: ISO standards such as ISO 42001 are voluntary international best practice standards. They are not legal compliance instruments with enforceable penalties for HR AI systems.

질문 # 142
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