CompTIA CY0-001 Dumps - Pass Exam With Ease [2026]

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CompTIA CY0-001 Exam Syllabus Topics:

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

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CY0-001 Test Quiz & CY0-001 Exam Vce

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CompTIA SecAI+ Certification Exam Sample Questions (Q31-Q36):

NEW QUESTION # 31
An AI security team must assess the probability of an attack on its new system and the impact associated with such an attack. Which of the following threat-modeling resources best addresses the threat landscape for machine learning (ML)?

A. Open Worldwide Application Security Project (OWASP)
B. Massachusetts Institute of Technology (MIT) risk repository
C. MITRE Adversarial Threat Landscape for AI Systems (ATLAS)
D. Common Vulnerabilities and Exposures (CVE) AI working group

Answer: C

Explanation:
MITRE ATLAS is specifically designed to capture adversarial tactics, techniques, and procedures (TTPs) targeting machine learning systems. It helps organizations assess both the probability and impact of AI/ML-related attacks, making it the most relevant threat-modeling resource.

NEW QUESTION # 32
An automobile manufacturer implements a chatbot to assist with configuration options for customer automobiles. Given a customer ' s prompt, the chatbot gives offensive responses.
Which of the following describes this behavior?

A. Model theft
B. Jailbreaking
C. Insecure output handling
D. Model skewing

Answer: B

NEW QUESTION # 33
A financial organization implements a new AI-based fraud detection system to flag suspicious transactions. A security analyst discovers that it occasionally blocks legitimate transactions.
Which of the following is the best recommendation?

A. Encrypting all the data processed by AI and applying further access controls
B. Implementing AI token usage and rate limits
C. Retaining the model with more data and recent transaction patterns
D. Rolling back the model and using a traditional fraud detection system

Answer: C

Explanation:
False positives occur when the AI model lacks sufficient or representative training data.
Retraining the model with more diverse and recent transaction patterns improves accuracy, reducing the chance of legitimate transactions being incorrectly flagged.

NEW QUESTION # 34
Which of the following controls is the best way to mitigate a denial-of-service (DoS) attack?

A. End-to-end encryption
B. Rate limiting
C. Access controls
D. Model guardrails

Answer: B

Explanation:
Rate limiting restricts the number of requests within a set timeframe, preventing attackers from overwhelming the system with excessive traffic, making it the best control to mitigate a DoS attack.

NEW QUESTION # 35
A security analyst is aware of an active penetration test in the environment. The analyst examines SIEM log data and notices the following AI system output:

Which of the following is the vulnerability that has occurred and the control the analyst should implement?

A. The vulnerability is prompt injection, and the analyst should use endpoint detection response (EDR).
B. The vulnerability is model hallucinations, and the analyst should develop output validations.
C. The vulnerability is sensitive information disclosure, and the analyst should employ masking.
D. The vulnerability is role impersonation, and the analyst should use validation.
E. The vulnerability is jailbreaking, and the analyst should utilize role-based access control.

Answer: C

Explanation:
Basic Concept: AI systems can inadvertently reveal sensitive information such as PII, credentials, or internal data in their outputs when not properly controlled. Sensitive information disclosure is a critical OWASP LLM Top 10 risk. CompTIA SecAI+ Study Guide covers both vulnerability identification and appropriate data protection controls for AI outputs.
Why D is Correct: The scenario describes the AI system outputting sensitive information in its responses, which is a sensitive information disclosure vulnerability. The appropriate control is masking, which replaces sensitive data values such as credit card numbers, SSNs, or API keys with redacted or tokenized equivalents in the model ' s outputs before they are returned to users. This prevents the AI from disclosing sensitive data while still providing useful responses.
Why A is Wrong: Prompt injection involves crafting inputs to override model instructions. If the penetration test revealed sensitive information, the primary vulnerability is the disclosure of that sensitive data, not the injection mechanism itself. EDR monitors endpoint behavior, not AI output content.
Why B is Wrong: Model hallucinations produce fabricated information rather than disclosing real sensitive data. The described scenario involves actual sensitive information being revealed, not fictitious content generation.
Why C is Wrong: Jailbreaking circumvents safety restrictions but the primary harm demonstrated is sensitive data exposure. RBAC manages access permissions but does not prevent the model from including sensitive data in responses once access is granted.
Why E is Wrong: Role impersonation involves the AI pretending to be a different entity. This may be a secondary technique used by the penetration tester but the primary vulnerability described is the disclosure of actual sensitive information in the output.

NEW QUESTION # 36
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