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| Section | Weight | Objectives |
|---|---|---|
| Basic AI Concepts Related to Cybersecurity | 17% | - Generative AI Concepts
|
| AI Governance, Risk, and Compliance | 19% | - AI Governance Frameworks
|
| AI-Assisted Security | 24% | - Security Operations Enhancement
|
| Securing AI Systems | 40% | - AI System Protection
|
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NEW QUESTION # 84
An attacker successfully completes a denial-of-service (DoS) attack through the context window of an AI system. Thousands of characters are obfuscated and hidden behind an emoji. Which of the following techniques best mitigates this type of attack?
Answer: B
Explanation:
A DoS attack through the context window relies on overwhelming the model with excessive or obfuscated input. Prompt filtering prevents such malicious or oversized inputs from being processed, ensuring that the model only receives safe, properly structured data within acceptable limits.
NEW QUESTION # 85
An organization is concerned with the exposure of sensitive data.
Which of the following is the most relevant security concern?
Answer: A
Explanation:
Basic Concept: AI models can inadvertently memorize sensitive information from their training data. Certain attack techniques can exploit this memorization to extract private information from a deployed model, even without direct access to the training dataset. CompTIA SecAI+ Study Guide covers model inversion as an AI- specific data exposure attack vector.
Why B is Correct: Model inversion is an attack where an adversary queries a deployed AI model with carefully crafted inputs to reconstruct or infer sensitive training data. For example, an attacker could query a facial recognition model with optimized images to reconstruct faces of individuals from the training set, or query a medical diagnosis model to infer patient records used in training. This directly exposes sensitive data that was supposed to be protected.
Why A is Wrong: Overfitting is a model training quality issue where a model learns training data too specifically and performs poorly on new data. While it can indicate that sensitive data was memorized, overfitting itself is a performance concern rather than directly a data exposure attack vector.
Why C is Wrong: Data normalization is a preprocessing technique that scales numerical features to a common range to improve training performance. It is a data preparation step with no direct relevance to sensitive data exposure or privacy attacks.
Why D is Wrong: Hyperparameter tuning adjusts configuration parameters of a model to optimize its performance during training. It is an optimization technique with no relevance to protecting against sensitive data exposure attacks.
NEW QUESTION # 86
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: D
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 # 87
A manufacturing company wants to use AI within its operations to improve the efficiency and accuracy of its processes.
Which of the following should the organization do first to enable adoption and achieve the business objectives?
Answer: B
Explanation:
Basic Concept: Successful AI adoption requires human expertise to translate business objectives into appropriate technical AI strategies. Before selecting tools, frameworks, or certifications, organizations need qualified professionals who can assess requirements, design solutions, and guide implementation. CompTIA SecAI+ Study Guide addresses AI adoption governance and role responsibilities.
Why B is Correct: Hiring a data and AI architect is the essential first step because this role bridges business requirements and technical AI capabilities. The architect assesses the organization ' s data maturity, identifies appropriate AI use cases aligned with manufacturing objectives, designs the technical architecture, and guides technology selection. Without this expertise, subsequent decisions about models, certifications, or frameworks may be poorly aligned with actual business needs.
Why A is Wrong: ISO 42001 certification for AI management systems is an appropriate governance milestone but requires an existing AI program to certify. Pursuing certification before establishing AI capabilities and expertise puts the governance cart before the operational horse.
Why C is Wrong: Selecting an LLM before understanding the organization ' s specific use cases, data landscape, and technical requirements is premature. LLMs may not even be the appropriate AI technology for manufacturing process optimization, which often benefits more from computer vision or predictive analytics.
Why D is Wrong: Introducing a GAN before conducting a needs assessment and hiring qualified architects is technology-first thinking that ignores whether GANs address the specific manufacturing efficiency and accuracy objectives. GANs are also specialized architectures not suited for general manufacturing process improvement.
NEW QUESTION # 88
A security operations center (SOC) has a very high volume of logs and alerts. The manager proposes the implementation of machine learning (ML) system to help with triage. Which of the following tasks is most suitable?
Answer: C
Explanation:
Machine learning is best suited for analyzing large volumes of security data and distinguishing between true threats and false positives. By identifying and classifying alerts, the ML system helps the SOC prioritize incidents and reduce analyst workload.
NEW QUESTION # 89
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For candidates who prefer a more flexible and convenient option, CompTIA provides the CY0-001 PDF file, which can be easily printed and studied at any time. The PDF file contains the latest real CompTIA SecAI+ Certification Exam (CY0-001) questions, and CY0-001 ensures that the file is regularly updated to keep up with any changes in the exam's content.
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