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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Securing AI Systems | 40% | - AI System Protection
|
| Topic 2: AI Governance, Risk, and Compliance | 19% | - Risk and Compliance
|
| Topic 3: AI-Assisted Security | 24% | - Security Operations Enhancement
|
| Topic 4: Basic AI Concepts Related to Cybersecurity | 17% | - Generative AI Concepts
|
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問題 #35
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?
答案：B
解題說明：
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.
問題 #36
A security analyst receives an alert about an AI system and is investigating the following output:
Which of the following is the most appropriate control the analyst should recommend?
答案：C
解題說明：
Basic Concept: Suspicious or unexpected AI system outputs are often caused by malicious or malformed user inputs that exploit the AI ' s input processing. Validating and sanitizing user inputs before they reach the AI model prevents many classes of attacks including prompt injection, data exfiltration attempts, and input manipulation. CompTIA SecAI+ Study Guide emphasizes input validation as a foundational AI security control.
Why B is Correct: Implementing user input validation applies checks and constraints to all user-submitted content before it is processed by the AI system. Input validation can enforce length limits, detect injection patterns, filter disallowed characters or command structures, and ensure inputs conform to expected formats.
By rejecting malicious or malformed inputs at the entry point, this control prevents them from reaching the model and causing the suspicious outputs observed.
Why A is Wrong: Data sanitization processes data to remove harmful elements and is closely related to validation. However, input validation is broader and more proactive, checking conformance to rules before processing, while sanitization typically operates during or after processing. Validation at the input boundary is the more appropriate first-line control.
Why C is Wrong: Monitoring logs for attack keywords is a detective control that identifies attacks after they have already affected the system. It does not prevent suspicious outputs from being generated in the first place.
Why D is Wrong: Hardening the Model Context Protocol server improves the security of the infrastructure hosting the AI components. While important for infrastructure security, it does not directly validate or inspect the content of user inputs that cause suspicious outputs.
問題 #37
What control reduces the impact radius when a single host is compromised?
答案：B
解題說明：
Segmentation isolates systems and limits lateral movement.
問題 #38
Which of the following is an example of how a security analyst uses generative AI in the triage process?
答案：C
解題說明：
In triage, generative AI is most effective for quickly summarizing and categorizing large volumes of alerts or findings. This helps analysts prioritize incidents and streamline the investigation process.
問題 #39
A cybersecurity analyst wants to choose a machine learning (ML) model to classify log entries while providing the best explainability. Which of the following models should the analyst use?
答案：A
解題說明：
Decision trees provide clear and interpretable decision paths, making them highly explainable compared to complex models like neural networks or GANs. This makes them the best choice for classifying log entries when explainability is a priority.
問題 #40
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