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| Section | Weight | Objectives |
|---|---|---|
| AI-Assisted Security | 24% | - Security Operations Enhancement
|
| AI Governance, Risk, and Compliance | 19% | - Risk and Compliance
|
| Basic AI Concepts Related to Cybersecurity | 17% | - AI Threat Landscape
|
| Securing AI Systems | 40% | - Adversarial Defense
|
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NEW QUESTION # 143
An administrator must conduct generative AI cost monitoring for use in the healthcare industry.
Which of the following criteria is the best way to calculate this cost?
Answer: D
Explanation:
Basic Concept: Generative AI systems in healthcare settings incur costs from multiple operational activities.
Understanding the cost drivers specific to generative AI helps administrators implementaccurate cost monitoring and controls. CompTIA SecAI+ Study Guide covers AI cost management under securing AI systems.
Why C is Correct: Storage retrieval and prompt processing are the two primary cost drivers for generative AI systems in healthcare. Storage retrieval refers to the cost of querying vector databases or document stores in RAG-based AI systems to fetch relevant patient records, clinical guidelines, or historical data for context.
Prompt processing encompasses the token-based cost of the LLM processing the combined retrieved content and user query to generate a response. Together these two activities represent the billable units that drive generative AI costs in healthcare RAG deployments, making them the most accurate basis for cost calculation and monitoring.
Why A is Wrong: Connection access and exchange gateway costs relate to network infrastructure and API gateway usage fees. While there may be minor costs associated with API calls, these are not the primary cost drivers for generative AI systems where the dominant expenses are computational token processing and data retrieval operations.
Why B is Wrong: Encryption and decryption processing costs relate to cryptographic operations for data security. While encryption is important for healthcare data protection under HIPAA, cryptographic processing overhead is minimal compared to the substantial token-based LLM processing and storage retrieval costs that dominate generative AI operational expenses.
Why D is Wrong: Catalog servicing and exchange processing are terms associated with data catalog management and data exchange infrastructure. These are not recognized primary cost components of generative AI systems in healthcare, where storage retrieval and token-based prompt processing are the established cost measurement criteria.
NEW QUESTION # 144
An AI architect reviews AI utilization and wants to improve the user experience. Which of the following should the architect review within the logs?
Answer: B
Explanation:
To improve user experience, the architect should review model accuracy in the logs. High accuracy ensures users receive relevant, reliable responses, directly impacting satisfaction and effectiveness of the AI system.
NEW QUESTION # 145
Which of the following is the most appropriate reason for model validation?
Answer: B
Explanation:
Model validation is intended to determine systematically whether an AI or machine learning model performs according to its defined objectives and produces acceptable results. This requires a formal methodology, appropriate validation data, measurable criteria, and comparison of actual model behavior against expected outcomes. CompTIA SecAI+ explicitly includes model validation among model-training techniques and also places validation, model evaluation, monitoring, and feedback within the AI lifecycle. Option C therefore represents the actual purpose of validation: objectively establishing whether the model satisfies predetermined performance and quality requirements. Providing feedback between interactions is closer to feedback and iteration or model adaptation rather than validation itself. Checking an exported file is merely an output or serialization check and does not evaluate model quality comprehensively. CI/CD integration checks establish whether software components build, integrate, or deploy correctly; a model could successfully pass those technical checks while still producing inaccurate, biased, unsafe, or otherwise unacceptable predictions.
Formal measurement against expected outcomes is therefore the distinguishing characteristic of proper model validation.
NEW QUESTION # 146
An architect is creating a threat model for an agentic system. Which of the following should the architect do first?
Answer: C
Explanation:
The first step in creating a threat model is to identify trust boundaries, which define where data or control transitions between different systems, users, or components. This helps map potential attack surfaces and informs subsequent risk analysis and control
NEW QUESTION # 147
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: A
Explanation:
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.
NEW QUESTION # 148
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