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| Section | Weight | Objectives |
|---|---|---|
| Securing AI Systems | 40% | - Adversarial Defense
|
| AI Governance, Risk, and Compliance | 19% | - AI Governance Frameworks
|
| AI-Assisted Security | 24% | - Operational Use of AI
|
| Basic AI Concepts Related to Cybersecurity | 17% | - Generative AI Concepts
|
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NEW QUESTION # 157
Which of the following controls is the best way to mitigate a denial-of-service (DoS) attack?
Answer: C
Explanation:
Basic Concept: DoS attacks overwhelm AI systems by sending excessive requests that exhaust computational resources, memory, or bandwidth, preventing legitimate users from being served. The primary defense against volume-based attacks is throttling the rate at which requests can be processed. CompTIA SecAI+ Exam Objectives identify rate limiting as the key DoS mitigation control for AI systems.
Why B is Correct: Rate limiting directly addresses the root mechanism of DoS attacks by restricting the number of requests any single client or IP address can submit within a defined time window. By enforcing request quotas, rate limiting prevents attackers from generating the request volume necessary to overwhelm the system while preserving capacity for legitimate users. It is the most direct and effective preventive control against DoS attacks on AI APIs and services.
Why A is Wrong: Model guardrails inspect and filter the content of prompts and responses for policy compliance and safety. They operate at the semantic content level, not at the request volume level, and cannot prevent resource exhaustion from high-volume request flooding.
Why C is Wrong: End-to-end encryption protects the confidentiality and integrity of data in transit. Encrypted DoS traffic is just as damaging as unencrypted traffic; encryption does not limit request rates or prevent resource exhaustion.
Why D is Wrong: Access controls restrict who can interact with the system, which can reduce the potential attacker pool. However, authenticated users and compromised accounts can still launch DoS attacks, and access controls alone cannot prevent high-volume attacks from authorized sources.
NEW QUESTION # 158
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: A
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 # 159
Faculty members at a university are concerned about potential inherent bias and inconsistency in one department's AI plagiarism detection service.
Which of the following principles will most likely to address their concerns?
Answer: D
Explanation:
Consistency ensures that an AI system applies rules and produces results in a uniform manner across all cases. This principle directly addresses concerns about bias and irregular outcomes in the plagiarism detection service.
NEW QUESTION # 160
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: A
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 # 161
Which of the following is the most appropriate reason for model validation?
Answer: C
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 # 162
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