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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Attacks, Threats, and Vulnerabilities | 24% | - Explain penetration testing concepts - Compare and contrast types of social engineering attacks - Given a scenario, analyze potential indicators associated with network attacks - Explain threat actor types and attributes - Given a scenario, analyze potential indicators to determine the type of attack - Explain vulnerability scanning concepts - Given a scenario, analyze potential indicators associated with application attacks |
| Topic 2: Governance, Risk, and Compliance | 14% | - Compare and contrast various types of security controls - Explain privacy and sensitive data concepts in relation to security - Explain risk management processes and concepts - Summarize regulations, standards, and frameworks that impact organizations - Given a scenario, follow organizational security policies and procedures |
| Topic 3: Architecture and Design | 21% | - Explain the importance of security concepts in an enterprise environment - Summarize basics of cryptographic concepts - Explain the importance of physical security controls - Given a scenario, implement cybersecurity resilience - Summarize virtualization and cloud security concepts - Explain secure application development, deployment, and automation concepts - Summarize authentication and authorization design concepts - Explain the security implications of embedded and specialized systems |
| Topic 4: Implementation | 25% | - Given a scenario, implement secure network architecture concepts - Given a scenario, implement identity and account management controls - Given a scenario, implement secure host settings - Given a scenario, implement secure mobile device policies - Given a scenario, implement authentication and authorization solutions - Given a scenario, implement secure systems design - Given a scenario, apply cybersecurity solutions to the cloud - Given a scenario, implement public key infrastructure (PKI) |
| Topic 5: Operations and Incident Response | 16% | - Given a scenario, use data sources to support an investigation - 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 appropriate tool to assess organizational security - Explain key aspects of digital forensics |
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12. Frage
An architect is creating a threat model for an agentic system. Which of the following should the architect do first?
Antwort: B
Begründung:
The first step in creating a threat model is to identify trust boundaries, which define where data or control transitions between different systems, users, or components. This helps map potential attack surfaces and informs subsequent risk analysis and control
13. Frage
A web server shows signs of SQL injection. Which control BEST prevents this?
Antwort: D
Begründung:
Validating and sanitizing user input is the core SQLi defense.
14. Frage
Which of the following strengthens the performance of a large language model (LLM) for malicious reconnaissance?
Antwort: D
Begründung:
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.
15. Frage
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.
Antwort:
Begründung:
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.
16. Frage
Security analysts want to track potential user behavior anomalies over time. Which of the following is the most comprehensive approach?
Antwort: B
Begründung:
Option D is correct because behavioral anomaly detection requires a persistent baseline of normal user activity and a repeatable method for measuring departures from that baseline. Automated playbooks can continuously collect authentication, endpoint, network, and application events, calculate deviations such as unusual login times, abnormal resource access, or atypical transaction volume, and escalate activity that exceeds defined thresholds. Standard-deviation-based comparison is broader and more defensible than searching for a username or checking a single data source. Option A provides only a permissions comparison at one point in time; it does not establish how the user normally behaves. Option B searches log occurrences but lacks behavioral context, trend analysis, and reliable anomaly scoring. Option C observes only browser activity and therefore misses anomalies in identity, host, cloud, and business systems. A mature implementation should tune baselines, account for role and seasonality, and retain analyst review so legitimate changes are not treated as malicious. This aligns with AI-assisted security concepts involving user and entity behavior analytics, automated enrichment, and risk-based triage.
17. Frage
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