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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Architecture and Design | 21% | - Explain secure application development, deployment, and automation concepts - Summarize virtualization and cloud security concepts - Explain the security implications of embedded and specialized systems - Summarize basics of cryptographic 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 |
| Topic 2: Operations and Incident Response | 16% | - Given a scenario, apply mitigation techniques or controls to secure an environment - Given a scenario, use data sources to support an investigation - Given a scenario, use appropriate tool to assess organizational security - Summarize the importance of policies, processes, and procedures for incident response - Explain key aspects of digital forensics |
| Topic 3: Governance, Risk, and Compliance | 14% | - Compare and contrast various types of security controls - Given a scenario, follow organizational security policies and procedures - Explain risk management processes and concepts - Summarize regulations, standards, and frameworks that impact organizations - Explain privacy and sensitive data concepts in relation to security |
| Topic 4: Implementation | 25% | - Given a scenario, implement identity and account management controls - Given a scenario, implement public key infrastructure (PKI) - Given a scenario, implement secure network architecture concepts - Given a scenario, implement authentication and authorization solutions - Given a scenario, implement secure systems design - Given a scenario, implement secure mobile device policies - Given a scenario, apply cybersecurity solutions to the cloud - Given a scenario, implement secure host settings |
| Topic 5: Attacks, Threats, and Vulnerabilities | 24% | - Compare and contrast types of social engineering attacks - Given a scenario, analyze potential indicators associated with application attacks - Explain threat actor types and attributes - Given a scenario, analyze potential indicators to determine the type of attack - Given a scenario, analyze potential indicators associated with network attacks - Explain vulnerability scanning concepts - Explain penetration testing concepts |
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91. 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: D
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.
92. Frage
A cybersecurity engineer implements a large language model (LLM) tool to automate an incident management workflow. However, the output contains items that do not exist in the real world. Which of the following is a technique to address this issue?
Antwort: D
Begründung:
The described problem is hallucination: the LLM is generating plausible-looking information that is not grounded in real-world facts. Retrieval-augmented generation is the best option because RAG supplements the model ' s prompt with information retrieved from an authoritative external knowledge source before the response is generated. This allows incident- management responses to be grounded in actual tickets, security documentation, threat intelligence, asset information, or other trusted records instead of relying exclusively on the model ' s internal parameters. CompTIA SecAI+ explicitly includes RAG, vector storage, and embeddings within its AI data concepts and also identifies hallucinations as an issue that organizations must monitor and audit. Watermarking helps identify AI-generated content but does not improve factual grounding. Output filtering can block prohibited or unsafe output patterns, but it cannot reliably supply missing factual information. Data rebalancing addresses uneven distributions in training datasets and is unrelated to grounding an LLM ' s incident-management responses. RAG therefore most directly reduces unsupported or fabricated information by giving the model relevant factual context at inference time.
93. Frage
A company deploys an internet-facing chatbot using RAG. Logs show that an administrator can retrieve employee names and usernames while an employee receives ' information not available. ' Which of the following is reducing the risk of sensitive data exposure in this scenario?
Antwort: B
Begründung:
Basic Concept: RAG-based AI systems retrieve information from knowledge bases to augment their responses. The differential access to sensitive employee data based on user role demonstrates that role-based data access controls are functioning correctly, restricting what data different users can retrieve through the AI interface. CompTIA SecAI+ Study Guide covers data access controls as the primary mechanism for preventing sensitive data exposure in RAG systems.
Why A is Correct: Data access controls define what information each user role is permitted to retrieve from the knowledge base. In this scenario, administrator-level users can access employee directory information while employee-level users cannot. The RAG system enforces these permissions when retrieving data for the AI ' s responses, preventing unauthorized users from accessing sensitive employee data through the chatbot interface regardless of how they phrase their queries.
Why B is Wrong: Model-specific guardrails filter responses based on content policies. While they can prevent certain categories of sensitive information from being disclosed, the scenario specifically shows differential access based on user role, which is the characteristic of access control enforcement, not content-based guardrail filtering.
Why C is Wrong: Rate limiting restricts request frequency. It does not differentiate what data different users can access; it only controls how often they can make requests. Both the administrator and employee could be subject to the same rate limit while still receiving different data based on their access controls.
Why D is Wrong: Prompt templates standardize how queries are structured. They do not implement user role- based data access restrictions or prevent specific user types from accessing sensitive information in the underlying knowledge base.
94. Frage
A healthcare organization plans to deploy a chatbot for appointment scheduling and patient records. Which of the following is the first step a security administrator should take?
Antwort: C
Begründung:
Before deploying an AI chatbot that will handle sensitive healthcare data, the first step is to conduct a risk assessment. This identifies potential threats, compliance requirements (such as HIPAA), and security gaps, ensuring proper controls are planned before implementation.
95. Frage
Which of the following job roles in an organizational governance structure develops a model from business use cases?
Antwort: C
Begründung:
Basic Concept: In AI governance, each role holds distinct responsibilities. Understanding these roles is core to CompTIA SecAI+ Domain 4 (AI Governance, Risk, and Compliance).
Why D is Correct: The Data Scientist is responsible for translating business use cases into working AI/ML models. They analyze business requirements, identify the appropriate machine learning approach, and develop models that fulfill specific business objectives. According to the CompTIA SecAI+ Study Guide, data scientists bridge raw data and actionable AI solutions by building and validating models derived from business-driven needs.
Why A is Wrong: A Platform Architect designs and manages the infrastructure and technical platforms hosting AI systems. Their focus is architectural design of the environment, not model development from business use cases.
Why B is Wrong: An AI Risk Analyst identifies, evaluates, and mitigates risks associated with AI adoption.
Their role is governance and risk-oriented, not model creation.
Why C is Wrong: An MLOps Engineer operationalizes, deploys, monitors, and maintains AI models in production. They take models already built by data scientists and ensure reliable operation at scale, not develop them from business use cases.
96. Frage
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