참고: DumpTOP에서 Google Drive로 공유하는 무료, 최신 CY0-001 시험 문제집이 있습니다: https://drive.google.com/open?id=1c6yxdWxu4KjrQhAUguvIJR_5hPwXH0Rl
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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: AI Governance, Risk and Compliance | 19% | - Risk management for AI
|
| Topic 2: AI-assisted Security | 24% | - AI in security strategy and operations
|
| Topic 3: Basic AI Concepts Related to Cybersecurity | 17% | - AI-driven threats and risks
|
| Topic 4: Securing AI Systems | 40% | - Defending against AI-specific attacks
|
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질문 # 18
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?
정답：E
설명：
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.
질문 # 19
A company discovers that attackers exploited an unpatched vulnerability in a web server. Which control BEST prevents this?
정답：A
설명：
Timely patching directly prevents exploitation of known vulnerabilities.
질문 # 20
An employee wants a consulting company to procure a data set that contains age, ethnicity, and diabetes status. During development, the employer wants to ensure the integrity of the data.
Which of the following is the best strategy to accomplish this task?
정답：D
설명：
Basic Concept: Data integrity ensures that data has not been tampered with, corrupted, or modified during storage or transmission. For AI training data that is procured from external sources, cryptographic integrity verification is essential to confirm the data arrived unmodified. CompTIA SecAI+ Study Guide covers data integrity controls for AI data pipelines.
Why A is Correct: Implementing checksums provides cryptographic verification of data integrity. A checksum or hash value such as SHA-256 is computed from the dataset at the source. The receiver computes the same hash and compares it to the provided value. Any modification to the data during transit or storage will produce a different hash, immediately detecting tampering or corruption. This is the most reliable, automated, and scalable strategy for ensuring the integrity of procured training data.
Why B is Wrong: Human evaluation can verify data quality and relevance but is impractical for verifying integrity across large datasets of medical records. Human reviewers cannot detect subtle bit-level corruption or intentional small modifications, and the process is not scalable.
Why C is Wrong: Querying the model tests model performance rather than verifying the integrity of the underlying training data. The model cannot tell you whether its training data was modified after collection or during procurement.
Why D is Wrong: Log monitoring tracks system activities and events over time. While useful for auditing access to data, it cannot retroactively confirm that data content has not been modified and does not provide cryptographic integrity guarantees.
질문 # 21
A security administrator sees suspicious queries on AI logs.
Which of the following should the administrator implement to address this issue?
정답：A
설명：
Basic Concept: Suspicious queries in AI system logs indicate that potentially malicious or policy-violating prompts are reaching the AI model. Proactively intercepting and filtering suspicious prompts before they are processed requires a prompt-level security control. CompTIA SecAI+ Study Guide identifies prompt firewalls as the appropriate control for blocking suspicious AI queries.
Why A is Correct: A prompt firewall analyzes incoming queries using a combination of pattern matching, semantic analysis, and policy rules to identify and block suspicious prompts before they reach the AI model.
It can detect prompt injection attempts, jailbreaking patterns, sensitive data extraction queries, and other suspicious prompt characteristics. By intercepting malicious prompts at the perimeter, it prevents them from influencing model behavior or extracting sensitive information.
Why B is Wrong: Data size controls limit the volume or size of data in requests. While controlling input size can prevent some attacks, it does not analyze the content or semantics of queries to detect suspicious patterns.
A small suspicious prompt can be just as harmful as a large one.
Why C is Wrong: Rate limiting controls the frequency of requests from a source. While it can slow down automated attack campaigns, it does not inspect query content for suspicious patterns and allows suspicious queries through as long as they are submitted below the rate threshold.
Why D is Wrong: Agentic AI is an AI architecture for autonomous multi-step task execution. It is a type of AI system, not a security control for filtering suspicious queries from an existing AI system ' s logs.
질문 # 22
An organization develops a chatbot that does not provide harmful or explicit responses, must use clean and professional language, and ensures that responses are accurate.
Which of the following should the organization conduct after the chatbot is fully developed but before a customer-facing deployment?
정답：C
설명：
Basic Concept: Before deploying an AI chatbot that has specific behavioral requirements - no harmful content, professional language, and accurate responses - organizations must verify that the controls designed to enforce these requirements actually work as intended. This pre-deployment verification is essential for customer-facing systems. CompTIA SecAI+ Study Guide covers guardrail testing as a required pre- deployment activity.
Why C is Correct: Guardrail testing and validation specifically verifies that the content filtering, safety controls, and behavioral constraints implemented in the chatbot function correctly before customer exposure.
This involves systematically testing with edge cases, adversarial prompts, and boundary conditions to confirm that harmful content is blocked, language remains professional, and responses are accurate. This directly validates the three requirements stated in the question.
Why A is Wrong: Data labeling and classification is a data preparation activity performed during model training and development. By the time the chatbot is fully developed, this work should already be complete.
Why B is Wrong: Model auditing and evaluation assesses overall model performance, accuracy, and compliance at a broader level. While important, it does not specifically verify that the guardrails enforcing the three behavioral requirements work correctly for the specific failure modes customers might trigger.
Why D is Wrong: Regression modeling and minimization refers to statistical techniques for continuous outcome prediction. This is not a relevant pre-deployment activity for a conversational chatbot requiring behavioral safety validation.
질문 # 23
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