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| Section | Weight | Objectives |
|---|---|---|
| Attacks, Threats, and Vulnerabilities | 24% | - Compare and contrast types of social engineering attacks - Explain vulnerability scanning concepts - Given a scenario, analyze potential indicators to determine the type of attack - Explain threat actor types and attributes - Given a scenario, analyze potential indicators associated with application attacks - Explain penetration testing concepts - Given a scenario, analyze potential indicators associated with network attacks |
| Implementation | 25% | - Given a scenario, implement secure host settings - Given a scenario, implement secure network architecture concepts - 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 systems design - Given a scenario, implement identity and account management controls - Given a scenario, apply cybersecurity solutions to the cloud |
| Architecture and Design | 21% | - Explain the security implications of embedded and specialized systems - Explain secure application development, deployment, and automation concepts - Summarize authentication and authorization design concepts - Explain the importance of security concepts in an enterprise environment - Summarize virtualization and cloud security concepts - Given a scenario, implement cybersecurity resilience - Explain the importance of physical security controls - Summarize basics of cryptographic concepts |
| Governance, Risk, and Compliance | 14% | - Compare and contrast various types of security controls - Summarize regulations, standards, and frameworks that impact organizations - Given a scenario, follow organizational security policies and procedures - Explain privacy and sensitive data concepts in relation to security - Explain risk management processes and concepts |
| Operations and Incident Response | 16% | - Summarize the importance of policies, processes, and procedures for incident response - Given a scenario, use data sources to support an investigation - Explain key aspects of digital forensics - Given a scenario, use appropriate tool to assess organizational security - Given a scenario, apply mitigation techniques or controls to secure an environment |
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質問 # 135
Which of the following strengthens the performance of a large language model (LLM) for malicious reconnaissance?
正解：C
解説：
Basic Concept: Reconnaissance is the information gathering phase of an attack. LLMs can be enhanced to perform more effective reconnaissance by giving them access to current, specific information beyond their training data cutoff. CompTIA SecAI+ covers AI augmentation techniques including RAG under AI-assisted security.
Why A is Correct: RAG enhances an LLM by connecting it to an external knowledge base or real-time data sources that it can query during inference. For reconnaissance purposes, a RAG-enabled LLM can access up- to-date organizational information, technical documentation, and intelligence feeds that go beyond its static training data. This makes the LLM significantly more capable for gathering current, targeted intelligence about specific organizations or infrastructure.
Why B is Wrong: Creating a web scraper is a basic data collection technique. While AI can help write scraper code, the scraper itself is a simple script that does not enhance the LLM ' s intelligence or reasoning capabilities for sophisticated reconnaissance.
Why C is Wrong: Instructing an AI assistant to query as an administrator is a prompt manipulation attempt.
An LLM cannot actually gain elevated permissions through a prompt instruction; this describes social engineering or privilege escalation via prompting, not a performance enhancement technique.
Why D is Wrong: Prompting a chatbot to describe naming patterns is a basic use of an existing LLM ' s knowledge. It does not strengthen or enhance the model ' s capabilities; it merely queries what the model already knows from training data, which may be outdated or generic.
質問 # 136
Hotspot Question
Instructions
Use the drop-down menus to define two appropriate security controls for each component of the AI system. Each control may be used only once.
If at any time you would like to bring back the initial state of the simulation please click the Reset All button.
An engineer is deploying a new AI system and wants to integrate it into the core system through an API.
正解：
解説：
Explanation:
Cloud control plane: IAM policies, Resource policies
Prompt firewall: Injection policies, Output monitoring
WAF: Connection rate limits, Input token validation
Front-end API + Model + Vector database: Guardrails, Input quota
API gateway: Load balancing, Authentication token validation
Cloud control plane relies on IAM and resource policies to enforce centralized identity and access management as well as usage restrictions.
Prompt firewall protects against malicious input via injection policies and ensures outputs are monitored for compliance and safety.
WAF controls connection rate to mitigate DoS and validates input tokens to stop unauthorized access attempts.
Front-end API, Model, and Vector database share guardrails to restrict unsafe behaviors and input quotas to prevent overuse, making them consistent and efficient security points.
API gateway enforces load balancing for availability and validates authentication tokens at the entry point, providing secure and scalable access.
質問 # 137
A recently deployed AI system becomes persistently unavailable. A restart temporarily fixes the issue, but the issue happens again. Upon examination of application programming interface (API) logs, an analyst finds that external calls continued to use system resources after the action completed.
Which of the following is the best way to improve availability of the system?
正解：D
解説：
The persistent unavailability is caused by external calls consuming resources even after completion, indicating sessions are not being closed. Enforcing session expiration ensures unused sessions are terminated, freeing resources and improving system availability.
質問 # 138
A detection engineering team wants to use AI to automatically prevent vulnerable code from reaching production.
Which of the following is the most effective way to accomplish this task?
正解：B
解説：
Basic Concept: Preventing vulnerable code from reaching production requires an automated, mandatory gate in the software delivery pipeline. The CI/CD pipeline is the enforcement point where all code must pass before deployment. CompTIA SecAI+ Study Guide covers AI integration in secure development pipelines under AI-assisted security.
Why C is Correct: Implementing an LLM in the CI/CD runner creates a mandatory automated security gate that every code change must pass. The LLM can analyze code for vulnerabilities, insecure patterns, and policy violations, then automatically fail the build if issues are found. This prevents vulnerable code from progressing toward production without human bypass capability, making it the most effective enforcement mechanism.
Why A is Wrong: IDE plug-ins provide warnings to developers during coding, but developers can choose to ignore them and proceed with compilation and commits. Warnings are advisory, not preventive, and cannot guarantee vulnerable code is blocked from the pipeline.
Why B is Wrong: SOAR platforms with ML models are excellent for incident response and security operations automation. However, they are not positioned in the code delivery pipeline and do not gate code from progressing to production.
Why D is Wrong: An agentic penetration testing tool validates vulnerabilities reactively after code is written or deployed. This approach does not intercept code before production deployment and is typically used for post-deployment assessment rather than prevention.
質問 # 139
A company is adopting AI and wants to create policies and procedures that include a structure for evaluating, publishing, and approving patterns for AI usage.
Which of the following should the company establish to meet this goal?
正解：C
解説：
Basic Concept: Successful AI adoption at an organizational level requires a centralized governance body that standardizes AI practices, promotes best practices, and ensures consistent, safe, and effective AI deployment across the organization. CompTIA SecAI+ Study Guide covers AI organizational governance structures under Domain 4.
Why A is Correct: An AI Center of Excellence (CoE) is an organizational unit specifically designed to govern, standardize, and advance AI adoption. It develops and publishes policies, creates approved patterns for AI usage, evaluates new AI use cases, provides expert guidance, and maintains governance oversight. The CoE exactly matches the described need for a structure to evaluate, publish, and approve AI usage patterns across the organization.
Why B is Wrong: An AI legal affairs office focuses on legal compliance, intellectual property, and regulatory matters related to AI. While important for legal risk management, it does not fulfill the broader governance mandate of establishing and approving AI usage patterns and best practices across the organization.
Why C is Wrong: An AI audit department conducts post-implementation reviews and compliance assessments of existing AI systems. It is a retrospective and oversight function rather than a proactive body for developing and approving AI usage patterns and policies.
Why D is Wrong: An AI data science division is a technical team focused on building AI models and solutions. It is a development function rather than a governance structure designed to create policies, evaluate AI patterns, and provide cross-organizational oversight of AI adoption.
質問 # 140
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