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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Basic AI Concepts Related to Cybersecurity | 17% | - Generative AI Concepts
|
| Topic 2: AI Governance, Risk, and Compliance | 19% | - AI Governance Frameworks
|
| Topic 3: Securing AI Systems | 40% | - AI System Protection
|
| Topic 4: AI-Assisted Security | 24% | - Security Operations Enhancement
|
CY0-001試験教材は、多くの人々がソフトパワーを向上させるのに役立ちました。彼らは同僚よりも効率的であるため、リーダーからより多くの注目を集めています。私たちは皆普通の専門家です。私たちは機会に値することを示すために力を示さなければなりません。 CY0-001練習エンジンを使用することは、体力を改善するための最も重要なステップです。バタフライ効果のように、あなたの選択の1つがあなたの人生に影響するかもしれないことを知っています。また、CY0-001試験の質問は、CY0-001試験に合格して夢のような認定を取得するための適切な試験ツールとなります。
質問 # 133
A penetration tester is assessing the controls of a deployed AI system that is designed to search and return the contents of files. The tester runs the following:
Which of the following is the best control to prevent abuse of the system?
正解：C
解説：
The penetration test shows that the system accepts arbitrary commands like deleteBuckets or listPermissions, which could lead to privilege abuse. The most effective control is least privilege, ensuring the service account only has access to what is strictly necessary (e.g., reading files) and not sensitive operations like deleting buckets or altering permissions.
質問 # 134
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?
正解：C
解説：
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.
質問 # 135
A legal firm permits employees to use a company-authorized chatbot for their research and reporting. In the past year, the firm had to retract some of its released documents because of incorrect references.
Which of the following best describes the weakness that caused this issue?
正解：C
解説：
Overreliance occurs when users place excessive trust in AI-generated output without performing sufficient independent verification. The scenario indicates that employees used a chatbot for research and reporting and subsequently published documents containing incorrect references. That pattern strongly indicates that AI- generated citations or factual claims were accepted without adequate human validation. CompTIA SecAI+ explicitly lists overreliance among attacks and weaknesses that security professionals must recognize when analyzing AI systems. Generative AI systems can produce hallucinated or inaccurate citations that appear credible, which is why human oversight and human validation remain important lifecycle controls. Insecure output handling instead concerns unsafe downstream processing of model output, such as executing generated code or passing untrusted output into another system without validation. Excessive agency occurs when an AI system has more autonomous permissions or capabilities than necessary. Sensitive information disclosure involves exposing protected or confidential information. None of those conditions explains publication of believable but inaccurate references as directly as overreliance. Appropriate mitigation includes independent verification, authoritative-source validation, human review, and monitoring AI output for accuracy and hallucinations before publication.
質問 # 136
A security analyst finds that the AI system is under a denial-of-wallet attack. Which of the following should the analyst enforce to protect the company? (Choose two.)
正解：B、C
解説：
API rate controls limit the number of requests within a set timeframe, preventing attackers from overloading the system and driving up costs.
Output token controls restrict the length of responses, reducing unnecessary token usage that attackers might exploit in a denial-of-wallet attack.
質問 # 137
A security engineer needs to monitor an AI-based system for runtime operations. The engineer is mostly concerned about the visibility of internal activity.
Which of the following is the most appropriate monitoring solution?
正解：D
解説：
Basic Concept: Monitoring an AI system ' s internal runtime behavior requires deep observability into what the system is doing at the code and function execution level, not just at the perimeter. CompTIA SecAI+ Study Guide addresses AI system observability and runtime monitoring under securing AI infrastructure.
Why D is Correct: Enabling stack call and debugging level traces at the function level provides the highest granularity of visibility into internal operations. This approach exposes what functions are called, in what order, with what inputs, and what is returned, offering genuine insight into the AI system ' s internal activity at runtime precisely as the engineer requires.
Why A is Wrong: A SIEM aggregates and correlates log and event data from multiple sources. While useful for security alerting, it does not inherently provide visibility into internal function-level operations of an AI model at runtime.
Why B is Wrong: A WAF with header logging monitors and filters HTTP traffic at the application boundary.
It captures external request and response data, not the AI system ' s internal runtime mechanics.
Why C is Wrong: Relying on vendor controls and monitoring prompt inputs is a passive, externally-focused approach. It provides no visibility into intermediate computations or internal operations within the AI model itself.
質問 # 138
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