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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Basic AI Concepts Related to Cybersecurity | 17% | - AI and Machine Learning Fundamentals
|
| Topic 2: Securing AI Systems | 40% | - Adversarial Defense
|
| Topic 3: AI-Assisted Security | 24% | - Operational Use of AI
|
| Topic 4: AI Governance, Risk, and Compliance | 19% | - AI Governance Frameworks
|
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NEW QUESTION # 128
Which of the following controls is the best way to mitigate a denial-of-service (DoS) attack?
Answer: B
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 # 129
A human resources officer is using AI to evaluate resumes and help select candidates that meet minimum criteria. To improve the results, the human resources officer adjusts the query parameters and includes an example resume that matches a successful candidate. Which if the following best describes this query?
Answer: C
Explanation:
One-shot prompting provides the model with a single example (in this case, a successful resume) to guide how it should process future inputs. This technique helps the AI better align its output with the desired evaluation criteria.
NEW QUESTION # 130
A machine learning (ML) engineer is working with a security engineer to identify the best practices for securing a system with various AI models.
Which of the following actions should the engineers suggest?
Answer: D
Explanation:
A secure MDLC is tailored to AI and ML systems, ensuring security is integrated throughout the model's design, training, validation, deployment, and monitoring phases. This directly addresses best practices for securing systems with AI models.
NEW QUESTION # 131
A management team is concerned about an unexpected cost increase for a public-facing AI chatbot.
Which of the following should a security administrator examine first to determine the root cause?
Answer: D
Explanation:
Basic Concept: AI chatbot operational costs are primarily driven by token consumption - the number of tokens processed in requests and generated in responses. Unexpected cost increases in LLM-based chatbots almost always trace back to abnormal token usage patterns. CompTIA SecAI+ Study Guide covers AI cost monitoring and token-based billing under securing AI systems.
Why D is Correct: Model token usage logs directly show how many tokens are being consumed per request, by which users or endpoints, and whether usage has increased abnormally. Examining token usage data is the most direct path to identifying the root cause of unexpected cost increases - whether from a denial-of-wallet attack, user abuse, a new feature generating verbose responses, or legitimate organic growth in usage. This is the first and most relevant examination point for LLM cost analysis.
Why A is Wrong: Firewall logs capture network-level traffic information. While they can reveal unusual access patterns or volumes, they do not contain token consumption data that directly explains LLM billing increases.
Why B is Wrong: WAF rules define filtering policies for web traffic. Reviewing rule configurations does not reveal whether token usage has increased or why costs have risen; it shows security policy settings rather than consumption metrics.
Why C is Wrong: Vector database IOPS performance measures how quickly the database processes read and write operations. While relevant to RAG system performance, IOPS metrics do not directly explain LLM API cost increases driven by token consumption.
NEW QUESTION # 132
Which of the following would most likely be used to prove that an image is AI generated?
Answer: B
Explanation:
Basic Concept: As AI-generated images become increasingly indistinguishable from authentic photographs, technical mechanisms are needed to definitively identify synthetic content. Embedding verifiable markers in AI-generated content at creation time provides a reliable provenance trail. CompTIA SecAI+ Study Guide covers digital content authentication and AI provenance controls.
Why D is Correct: Watermarking embeds invisible or visible identifying markers into AI-generated images at the point of creation. These watermarks can be cryptographic, steganographic, or perceptual in nature and survive compression, cropping, and other common image manipulations. When an image ' s provenance is questioned, watermark detection tools can analyze it to confirm AI generation. This is the primary technical mechanism for proving AI image origin as supported by the Content Authenticity Initiative and C2PA standards.
Why A is Wrong: Human validation relies on subjective visual inspection by experts to determine if an image appears AI-generated. Modern AI image generation has advanced to the point where human reviewers frequently cannot distinguish synthetic from authentic images, making this approach unreliable for definitive proof.
Why B is Wrong: Guardrails are input and output filtering controls for AI systems. They restrict what content an AI can generate but do not create verifiable proof of an image ' s AI origin after generation.
Why C is Wrong: Diffusion refers to the diffusion model architecture used to generate images. It describes the creation process, not a verification mechanism. Knowing that diffusion models were used does not help prove a specific image was AI-generated after the fact.
NEW QUESTION # 133
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