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| Section | Weight | Objectives |
|---|---|---|
| Architecture and Design | 21% | - Summarize basics of cryptographic concepts - Summarize virtualization and cloud security concepts - Explain secure application development, deployment, and automation concepts - Summarize authentication and authorization design concepts - Explain the importance of physical security controls - Explain the importance of security concepts in an enterprise environment - Given a scenario, implement cybersecurity resilience - Explain the security implications of embedded and specialized systems |
| Implementation | 25% | - Given a scenario, implement identity and account management controls - Given a scenario, implement secure host settings - Given a scenario, apply cybersecurity solutions to the cloud - Given a scenario, implement secure mobile device policies - Given a scenario, implement public key infrastructure (PKI) - Given a scenario, implement authentication and authorization solutions - Given a scenario, implement secure network architecture concepts - Given a scenario, implement secure systems design |
| Operations and Incident Response | 16% | - Given a scenario, use appropriate tool to assess organizational security - Explain key aspects of digital forensics - Given a scenario, apply mitigation techniques or controls to secure an environment - Summarize the importance of policies, processes, and procedures for incident response - Given a scenario, use data sources to support an investigation |
| Attacks, Threats, and Vulnerabilities | 24% | - Explain threat actor types and attributes - Given a scenario, analyze potential indicators to determine the type of attack - Explain vulnerability scanning concepts - Explain penetration testing concepts - Compare and contrast types of social engineering attacks - Given a scenario, analyze potential indicators associated with application attacks - Given a scenario, analyze potential indicators associated with network attacks |
| Governance, Risk, and Compliance | 14% | - Explain privacy and sensitive data concepts in relation to security - Given a scenario, follow organizational security policies and procedures - Compare and contrast various types of security controls - Explain risk management processes and concepts - Summarize regulations, standards, and frameworks that impact organizations |
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38. Frage
An airline corporation wants to implement a chatbot application using a large language model (LLM) so its customers can ask questions and receive answers about flight details and have the option to upload files.
Which of the following security controls should the airline use to protect against malicious input and unauthorized use beyond the service-level agreement? (Choose two.)
Antwort: A,B
Begründung:
Basic Concept: LLM-based chatbots accepting user-uploaded files face two critical risk categories: malicious input injection and resource or cost abuse. CompTIA SecAI+ Study Guide highlights prompt security controls and resource management as key defensive layers for public-facing LLM applications.
Why A is Correct: Prompt guardrails intercept and filter user inputs and model outputs, blocking malicious prompts, prompt injection attempts, and harmful file content before affecting model behavior. Since users can upload files, guardrails are essential for sanitizing and validating that content before processing.
Why D is Correct: Model token quotas directly limit how much of the LLM ' s processing capacity a user can consume. This prevents abuse beyond the SLA, including denial-of-wallet attacks or resource exhaustion through excessively large inputs or repeated requests.
Why B is Wrong: Role-based access controls manage who can access what resources. While useful for internal systems, they do not address malicious input content or enforce LLM resource consumption limits for a public-facing chatbot.
Why C is Wrong: Firewall rules operate at the network layer and can block unauthorized IPs or ports but cannot inspect or filter the semantic content of prompts or control token-level LLM usage.
39. Frage
A global security operations center (SOC) wants to adapt and leverage the strength of AI in order to enhance its security operations. Which of the following is the best way to enhance the global SOC functions?
Antwort: B
Begründung:
AI can significantly enhance SOC operations by summarizing and correlating high volumes of alerts, enabling analysts to quickly identify patterns, prioritize threats, and gain actionable insights. This reduces analyst fatigue and improves response times without introducing unsafe automation risks.
40. Frage
A security engineer needs to monitor an AI-based system for runtime operations. The engineer is mostly concerned about the visibility of internal activity. Which of the following is the most appropriate monitoring solution?
Antwort: C
Begründung:
For runtime visibility into internal activity of an AI system, the most suitable control is enabling stack calls and debugging-level traces. This provides granular insights into function-level execution, dependencies, and operations, which directly supports monitoring of runtime behavior.
41. Frage
A company wants to detect abnormal insider activity based on historical logs. Which technology is BEST?
Antwort: A
Begründung:
User and Entity Behavior Analytics detects deviations from normal patterns.
42. Frage
Which of the following is the best example of an AI model that is trained to identify multiple points from input using a neural network to provide output for authentication?
Antwort: B
Begründung:
Basic Concept: Neural networks can be trained to identify and match complex multi-point patterns in data.
For biometric authentication, facial recognition uses deep neural networks to extract and compare dozens to hundreds of facial feature points from an input image. CompTIA SecAI+ covers neural network-based authentication under basic AI concepts.
Why A is Correct: Facial recognition systems use neural networks specifically trained to identify multiple facial landmarks and feature points such as eye distance, nose shape, and jawline contour from input images.
These extracted feature vectors are compared against stored templates to authenticate individuals, making this the ideal example of multi-point neural network-based authentication output.
Why B is Wrong: An encryption key is a cryptographic artifact, not an AI model output. Encryption keys are generated mathematically, not through neural network training or multi-point feature identification from biometric inputs.
Why C is Wrong: OAuth is an open authorization protocol framework that handles delegated access and permission grants between services. It is an authentication delegation standard, not an AI model that processes input through neural networks.
Why D is Wrong: A bounding box is an output of object detection models that draws a rectangular box around detected objects in an image. While it uses neural networks, it identifies object location rather than multiple feature points for authentication purposes.
43. Frage
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