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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Securing AI Systems | 40% | - Secure AI development and operations
|
| Topic 2: Basic AI Concepts Related to Cybersecurity | 17% | - AI applications in security
|
| Topic 3: AI-assisted Security | 24% | - AI in security strategy and operations
|
| Topic 4: AI Governance, Risk and Compliance | 19% | - Compliance and legal requirements
|
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NEW QUESTION # 87
An organization recently developed an AI-powered product and discovers that it is vulnerable to attacks in which malicious actors can alter the input, causing the system to recommend inappropriate information. Which of the following techniques is the most effective way to secure the system against manipulation attacks?
Answer: D
Explanation:
Guardrails restrict and control how an AI model processes and responds to inputs, making them the most effective defense against manipulation attacks such as prompt injection or malicious input alteration.
NEW QUESTION # 88
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:
Basic Concept: Context window DoS attacks flood an LLM ' s context with obfuscated content to exhaust processing resources or manipulate model behavior. Attackers may hide large amounts of text behind Unicode characters like emojis. CompTIA SecAI+ Study Guide identifies prompt filtering as the primary defense against input-based attacks on LLMs.
Why D is Correct: A prompt filter inspects incoming inputs before they reach the LLM, detecting and blocking malicious content including obfuscated text hidden behind Unicode characters or emojis. By analyzing input structure, character counts, hidden content, and encoding anomalies, prompt filters can identify and reject attacks that attempt to abuse the context window, preventing resource exhaustion.
Why A is Wrong: Fraud detection systems are designed to identify fraudulent transactions or activities in structured data contexts. They are not designed to inspect LLM prompt structures for obfuscated content attacks on context windows.
Why B is Wrong: LLM-as-a-judge uses a secondary LLM to evaluate the quality or safety of another model ' s outputs. It operates post-generation and cannot prevent a DoS attack that occurs during input processing before output is generated.
Why C is Wrong: Pattern recognition can identify known attack patterns but requires the attack to match pre- learned patterns. Novel obfuscation techniques using Unicode or emoji hiding may evade pattern-based detection without dedicated prompt filtering logic.
NEW QUESTION # 89
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: C,F
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 # 90
An organization recently created a custom model that integrates with a language model (LLM). The developer notices that the application programming interface (API) costs have increased.
Which of the following is the best control to reduce cost?
Answer: A
Explanation:
Basic Concept: LLM API pricing is primarily based on token consumption - the number of tokens processed in both input prompts and output responses. Controlling token usage is the most direct lever for managing and reducing LLM API costs. CompTIA SecAI+ Study Guide covers AI cost management and resource controls under securing AI systems.
Why D is Correct: Adjusting token limits directly caps the maximum number of tokens used per request for both input and output. By setting appropriate token limits, the organization prevents excessively long prompts or verbose responses from consuming unnecessary tokens, directly translating to lower API costs and providing hard budget control.
Why A is Wrong: Prompt templates standardize how queries are structured, which can indirectly improve efficiency. However, they do not enforce a hard cap on token usage and cannot prevent costs from escalating with large volumes or verbose responses.
Why B is Wrong: Increasing CPU and memory addresses computational infrastructure performance on the client side. LLM API costs are billed by the API provider based on token usage, not on the client ' s hardware resources.
Why C is Wrong: Reducing model size means using a smaller, less powerful model version. While this may lower cost per token, it is a model selection decision, not an ongoing operational control that can be adjusted to manage cost in real time.
NEW QUESTION # 91
During an update, an AI system flags some potential compatibility issues and provides recommendations. An administrator reviews the recommendations before addressing the issues.
Which of the following processes describes this scenario?
Answer: A
Explanation:
Basic Concept: Human-in-the-loop is a design pattern where AI systems generate recommendations or decisions but require human review and approval before those recommendations are acted upon. This approach maintains human oversight and accountability in AI-assisted workflows. CompTIA SecAI+ Study Guide covers human-in-the-loop as a key responsible AI principle and operational pattern.
Why C is Correct: The scenario precisely describes the human-in-the-loop pattern: the AI system identifies potential issues and provides recommendations, but an administrator must review those recommendations before any action is taken. This deliberate inclusion of human judgment in the AI ' s decision or recommendation workflow ensures human oversight is maintained, which is the defining characteristic of the human-in-the-loop process.
Why A is Wrong: Data validation verifies that data meets expected quality standards and formats before being used in AI processing. It is a data quality control activity, not a workflow pattern describing human review of AI recommendations.
Why B is Wrong: Data preparation involves transforming raw data into a format suitable for AI model training or inference. It encompasses cleaning, normalizing, and formatting data, not the process of human review of AI-generated recommendations during system updates.
Why D is Wrong: Model evaluation assesses a model ' s performance against metrics such as accuracy, precision, and recall on test datasets. It is a technical assessment of model quality, not a workflow process where humans review AI-generated recommendations before acting on them.
NEW QUESTION # 92
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