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| Section | Weight | Objectives |
|---|---|---|
| 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 |
| Architecture and Design | 21% | - Explain the importance of security concepts in an enterprise environment - Summarize basics of cryptographic concepts - Summarize virtualization and cloud security concepts - Explain the importance of physical security controls - Explain secure application development, deployment, and automation concepts - Given a scenario, implement cybersecurity resilience - Explain the security implications of embedded and specialized systems - Summarize authentication and authorization design concepts |
| Implementation | 25% | - Given a scenario, implement secure mobile device policies - Given a scenario, implement identity and account management controls - Given a scenario, apply cybersecurity solutions to the cloud - Given a scenario, implement secure host settings - Given a scenario, implement secure systems design - Given a scenario, implement secure network architecture concepts - Given a scenario, implement authentication and authorization solutions - Given a scenario, implement public key infrastructure (PKI) |
| Governance, Risk, and Compliance | 14% | - Summarize regulations, standards, and frameworks that impact organizations - Explain risk management processes and concepts - 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 |
| Attacks, Threats, and Vulnerabilities | 24% | - Explain threat actor types and attributes - Compare and contrast types of social engineering attacks - Explain penetration testing concepts - Given a scenario, analyze potential indicators associated with network attacks - Explain vulnerability scanning concepts - Given a scenario, analyze potential indicators associated with application attacks - Given a scenario, analyze potential indicators to determine the type of attack |
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86. Frage
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?
Antwort: A
Begründung:
Integrating an LLM into the CI/CD pipeline ensures automated code review at build time, blocking vulnerable code from reaching production. This enforces security checks consistently and effectively during the deployment process.
87. Frage
A security operations center (SOC) has a very high volume of logs and alerts. The manager proposes the implementation of machine learning (ML) system to help with triage. Which of the following tasks is most suitable?
Antwort: D
Begründung:
Machine learning is best suited for analyzing large volumes of security data and distinguishing between true threats and false positives. By identifying and classifying alerts, the ML system helps the SOC prioritize incidents and reduce analyst workload.
88. Frage
A short AI-generated video shows a celebrity ' s likeness talking about a fake public security event.
Which of the following was used to create this video?
Antwort: A
Begründung:
Basic Concept: Creating realistic deepfake videos that convincingly replicate a real person ' s facial expressions, movements, and voice requires deep learning models capable of learning and synthesizing complex spatial and temporal features from existing video data. CompTIA SecAI+ covers deepfake technologies under basic AI concepts.
Why B is Correct: Convolutional Neural Networks are foundational to deepfake video generation. CNNs excel at learning spatial features from visual data and are used within deepfake architectures to analyze source and target faces, extract facial features, and synthesize realistic face swaps or face animations. Modern deepfake systems typically combine CNNs with autoencoders and GANs to generate convincing video content showing a person saying or doing things they never did.
Why A is Wrong: Statistical analysis involves mathematical methods for analyzing data distributions and relationships. It does not have the capability to generate synthetic video content or replicate a person ' s visual likeness in motion.
Why C is Wrong: An ML classifier assigns input data to predefined categories. Classification models detect and label content rather than generating new synthetic video content of a person ' s likeness. They are detection tools, not generation tools.
Why D is Wrong: Random forest is an ensemble ML method using multiple decision trees for classification and regression tasks. It works on structured, tabular data and cannot process or generate visual, spatial data needed for realistic deepfake video synthesis.
89. Frage
An organization implements a domain-specific AI chatbot. After operating normally for weeks, the model returns contextually incorrect responses - treating ' worm ' as a biological pest rather than a computer worm when answering a cybersecurity question.
Which of the following should the organization do to address the issue?
Antwort: C
Begründung:
Basic Concept: Domain-specific AI chatbots can produce contextually inappropriate responses when they lack sufficient domain grounding to disambiguate terms that have different meanings in different contexts.
Guardrails can enforce domain-appropriate interpretation and response constraints. CompTIA SecAI+ Study Guide covers guardrails as a mechanism for maintaining model behavioral boundaries.
Why A is Correct: Configuring guardrails allows the organization to enforce domain-specific behavioral constraints on the chatbot, ensuring it interprets ambiguous terms within the correct technical context.
Guardrails can include context-aware rules that recognize when a query is in a cybersecurity context and constrain the model to provide domain-appropriate responses. This directly addresses the issue of the model providing biologically-framed responses to a technical cybersecurity question.
Why B is Wrong: Encrypting model weights at rest protects the model parameters from unauthorized access or modification. It is a data protection control for model intellectual property and does not influence how the model interprets or responds to domain-specific queries at inference time.
Why C is Wrong: Model access controls restrict who can query and modify the model. They manage authorization at the user and system level but do not enforce domain-appropriate response constraints or prevent contextually incorrect answers from being generated.
Why D is Wrong: Prompt templates provide structured, reusable formats for common queries. While they can help standardize how cybersecurity questions are asked, they require users to use the template and do not provide real-time enforcement of domain-appropriate response generation for all input variations.
90. Frage
A security team is using an AI-based tool to try to bypass organizational boundaries. The team uses AI to look at the current state and suggest different attack vectors based on the outcome of the previous ones.
Which of the following techniques is the team most likely using?
Antwort: A
Begründung:
Basic Concept: Modern penetration testing increasingly leverages AI to automate the reconnaissance, exploitation, and pivoting process. AI-assisted automated penetration testing can adapt its strategy based on previous results, simulating intelligent adversary behavior more realistically than static scripts. CompTIA SecAI+ covers AI-assisted offensive security techniques.
Why D is Correct: Automated penetration testing uses AI to systematically discover and attempt to exploit vulnerabilities while adapting tactics based on the results of previous attempts. The described behavior - looking at the current state, suggesting attack vectors, and adjusting based on outcomes - precisely describes an adaptive AI-driven penetration testing tool that iteratively explores the attack surface, mimicking how an advanced persistent threat would operate.
Why A is Wrong: Manual signature matching compares network traffic or files against a database of known threat signatures. It is a passive detection technique used by defensive tools like IDS/IPS, not an adaptive offensive technique for bypassing organizational boundaries.
Why B is Wrong: Code quality testing analyzes source code for bugs, vulnerabilities, and adherence to coding standards. It is a development quality assurance activity, not an offensive security technique for testing organizational security boundaries.
Why C is Wrong: Fraud detection uses ML to identify suspicious patterns in transactions or user behavior for defensive purposes. It is a preventive security measure, not an offensive technique for penetration testing.
91. Frage
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