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CompTIA CY0-001 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|
| Basic AI Concepts Related to Cybersecurity | 17% | - 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. Threat detection and anomaly analysis
- 2. Security automation and decision support
|
| AI Governance, Risk and Compliance | 19% | - Risk management for AI
- 1. Risk mitigation and control strategies
- 2. AI risk identification and assessment
- Compliance and legal requirements
- 1. Transparency, accountability and auditability
- 2. Data protection and privacy laws
- 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 Security | 24% | - AI for threat detection and response
- 1. Automated incident triage and correlation
- 2. Accelerated threat hunting
- 3. Anomaly detection and behavioral analysis
- Security automation and orchestration
- 1. Vulnerability management and assessment
- 2. Workflow automation and response playbooks
- AI in security strategy and operations
- 1. Threat modeling and risk assessment
- 2. Compliance monitoring and auditing
|
| Securing AI Systems | 40% | - Defending against AI-specific attacks
- 1. Prompt injection, data poisoning, model inversion
- 2. Adversarial example defense
- 3. Threat modeling for AI lifecycles
- Secure AI development and operations
- 1. Secure MLOps and AI pipeline design
- 2. DevSecOps integration for AI
- Security controls for AI systems
- 1. Deployment environment security
- 2. Model security: access, integrity, anti-tampering
- 3. Data protection: integrity, confidentiality, privacy
|
>> CY0-001考題資訊 <<
CY0-001考題資訊 - 你通過考試最佳的利劍CompTIA SecAI+ Certification Exam
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最新的 CompTIA SecAI+ CY0-001 免費考試真題 (Q136-Q141):
問題 #136
A security analyst who is reviewing logs notices the following:
* Suspicious inputs are present and contain character look-alikes.
* Strange character substitutions
* Odd spacing
* Inputs that appear to be broken sentence structures
Which of the following best explains what the user was utilizing?
- A. Misinformation
- B. Disinformation
- C. Obfuscation
- D. Anonymization
答案:C
解題說明:
Obfuscation is the correct answer because the observed inputs show deliberate attempts to make content difficult for automated security controls or AI systems to interpret while preserving enough meaning for the intended recipient. Character look-alikes, unusual Unicode substitutions, abnormal spacing, and deliberately fragmented sentence structures are common techniques for disguising malicious or restricted content.
CompTIA SecAI+ explicitly identifies obfuscation within the AI-assisted security domain as a way AI can enable or enhance attack vectors.
Disinformation involves intentionally spreading false information to deceive an audience, while misinformation refers to inaccurate information that may be distributed without deliberate malicious intent.
Neither term describes manipulating the representation of text to evade detection. Anonymization removes or obscures identifying information to protect identity or privacy; it does not explain character substitution or malformed sentence construction. In this scenario, the attacker is manipulating how the input appears so filtering, pattern-matching, or other inspection mechanisms may have difficulty recognizing the underlying content. Therefore, obfuscation most precisely describes the activity.
問題 #137
Which of the following strengthens the performance of a large language model (LLM) for malicious reconnaissance?
- A. Enhancing a foundational model with the inclusion of retrieval-augmented generation (RAG)
- B. Creating a web scraper script using AI to capture the company website
- C. Prompting a chatbot to describe server naming patterns and Internet Protocol (IP) ranges
- D. Instructing an AI assistant to query as an administrator
答案:A
解題說明:
RAG augments an LLM with external, up-to-date information retrieval, improving its factual accuracy and contextual scope-capabilities that directly strengthen the model's effectiveness for reconnaissance.
問題 #138
Global IPs attempt logins against thousands of accounts with known breached credentials. What attack is occurring?
- A. SQL injection
- B. Credential stuffing
- C. DDoS
- D. Password spraying
答案:B
解題說明:
Credential stuffing uses leaked username-password pairs.
問題 #139
Which methods identify vulnerabilities BEFORE deployment? (Choose two.)
- A. Penetration testing
- B. Static code analysis
- C. Design reviews
- D. Patch management
- E. Behavior analytics
答案:B,C
解題說明:
Pre-deployment assessments rely on design evaluation and code inspection.
問題 #140
A customer using a travel chatbot reports that the chatbot responds with the following:
def choose_location(location):
if location == " United States of America " :
print( " You have selected an authorized travel destination " )
elif location == " Europe " :
print( " You have selected an unauthorized travel destination " )
Which of the following remediates this issue?
- A. Deploying DLP to prevent sensitive information disclosure
- B. Using cookies to protect against server-side request forgery
- C. Implementing secure output handling
- D. Retraining the model to eliminate bias
答案:A
解題說明:
Deploying data loss prevention (DLP) is the best answer because the chatbot has exposed internal application logic rather than returning only the information intended for the customer. This represents a form of sensitive information disclosure , which CompTIA SecAI+ specifically identifies as an AI-system attack or failure condition. The objectives also emphasize data security controls such as classification, redaction, masking, and minimization to prevent inappropriate disclosure.
A DLP control can inspect generated responses for source code, confidential identifiers, credentials, internal business logic, or other restricted information and prevent that content from being returned externally.
Retraining the model to eliminate bias is unrelated because no discriminatory or statistically skewed behavior is shown. Secure output handling is important when model-generated output is consumed by downstream applications and could lead to injection or execution vulnerabilities. Here, however, the immediate issue is disclosure of internal information directly to a user. Cookies also do not address the problem; they are associated with maintaining web sessions and are not a general mitigation for server-side request forgery.
Therefore, DLP most directly addresses the exposed information.
問題 #141
......
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