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| Section | Weight | Objectives |
|---|---|---|
| AI-Assisted Security | 24% | - Security Operations Enhancement
|
| Basic AI Concepts Related to Cybersecurity | 17% | - AI Threat Landscape
|
| AI Governance, Risk, and Compliance | 19% | - AI Governance Frameworks
|
| Securing AI Systems | 40% | - AI System Protection
|
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NEW QUESTION # 111
Which of the following controls is the best way to mitigate a denial-of-service (DoS) attack?
Answer: A
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 # 112
Which of the following responsible AI standards refers to a principle that clearly states the reasons behind the decisions for a particular conclusion?
Answer: D
Explanation:
Basic Concept: Responsible AI encompasses several key principles governing how AI systems should behave to be trustworthy and ethical. These principles are distinct but related. Understanding their precise definitions is essential for CompTIA SecAI+ Domain 4 governance questions.
Why D is Correct: Explainability in responsible AI means the AI system can clearly articulate the specific reasons, factors, and logic that led to a particular decision or output. It answers the question " why did the AI make this specific decision? " For example, an explainable credit scoring AI would not only give a score but also explain which factors such as payment history or credit utilization contributed most to that specific score.
This directly matches the question ' s description of " clearly stating reasons behind decisions. " Why A is Wrong: Accountability refers to the ability to identify who is responsible for AI system decisions and their consequences. It addresses ownership and responsibility assignment rather than explaining the reasoning behind specific decisions.
Why B is Wrong: Auditability refers to the ability to examine and verify an AI system ' s decisions, processes, and outputs through systematic review. It enables after-the-fact verification but does not mean the system itself explains its reasoning.
Why C is Wrong: Transparency refers to openness about how an AI system works at a general level, including its purpose, capabilities, limitations, and the data it was trained on. It is broader than explainability and does not specifically address articulating reasons for individual decisions.
NEW QUESTION # 113
An organization deploys a browser-based AI plug-in to detect malicious websites and phishing links in corporate email.
Which of the following techniques is used in this AI plug-in?
Answer: C
Explanation:
Basic Concept: AI-based security tools for detecting malicious websites and phishing links operate by analyzing URLs, page content, and link characteristics against known malicious patterns and behavioral signatures. CompTIA SecAI+ Study Guide covers pattern recognition and signature matching as fundamental AI-assisted threat detection techniques.
Why B is Correct: Pattern recognition and signature matching are the core techniques used in malicious website and phishing link detection. The AI plug-in uses pattern recognition to identify characteristics of phishing pages such as login form structures mimicking legitimate sites, suspicious domain patterns, and redirect behaviors. Signature matching compares URLs and page content against databases of known malicious sites and phishing infrastructure. Together these techniques enable accurate detection of threats in email links before users click them.
Why A is Wrong: Code quality testing analyzes source code for bugs, vulnerabilities, and adherence to coding standards during software development. It has no application for detecting malicious websites or phishing links in real-time email scanning.
Why C is Wrong: Automated penetration testing proactively exploits vulnerabilities to assess security posture.
It is an offensive security assessment technique, not a real-time threat detection technique for identifying malicious links in email.
Why D is Wrong: Automated incident response executes predefined response actions when security incidents are detected, such as isolating endpoints or blocking users. It operates after threats are detected, not during the detection phase that identifies malicious websites and links.
NEW QUESTION # 114
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.)
Answer: B,D
Explanation:
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.
NEW QUESTION # 115
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?
Answer: C
Explanation:
Basic Concept: LLMs process and generate text as discrete units called tokens, which represent words or word pieces. The number of tokens consumed is the primary metric for LLM resource utilization and billing.
Understanding token consumption is essential for both performance monitoring and cost management.
CompTIA SecAI+ Study Guide covers token-based measurement under AI system management.
Why A is Correct: Tokens are the fundamental unit of measurement for LLM utilization. Both input tokens sent in prompts and output tokens generated in responses are counted. Token consumption directly correlates with computational resource usage, response time, and API cost. Monitoring token counts enables the company to establish usage baselines and detect the abnormal token consumption spikes associated with DoS attacks or denial-of-wallet attacks against the LLM.
Why B is Wrong: A transformer is the neural network architecture that underlies modern LLMs. It is an architectural description, not a measurable utilization metric. You cannot quantify LLM resource consumption in " transformers. " Why C is Wrong: Chain of thought is a prompting technique that encourages models to reason step-by-step before providing answers. It is an inference strategy for improving response quality, not a unit of measurement for monitoring LLM utilization or resource consumption.
Why D is Wrong: A prompt is the input text submitted to the LLM. While monitoring prompt volumes provides request count information, tokens provide a more granular and meaningful utilization metric because they capture both the complexity of inputs and the length of generated outputs.
NEW QUESTION # 116
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