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| Section | Weight | Objectives |
|---|---|---|
| Attacks, Threats, and Vulnerabilities | 24% | - Explain threat actor types and attributes - Explain vulnerability scanning concepts - Given a scenario, analyze potential indicators associated with network attacks - Compare and contrast types of social engineering attacks - Given a scenario, analyze potential indicators to determine the type of attack - Given a scenario, analyze potential indicators associated with application attacks - Explain penetration testing concepts |
| Governance, Risk, and Compliance | 14% | - Compare and contrast various types of security controls - Given a scenario, follow organizational security policies and procedures - Explain privacy and sensitive data concepts in relation to security - Summarize regulations, standards, and frameworks that impact organizations - Explain risk management processes and concepts |
| Architecture and Design | 21% | - Explain the importance of security concepts in an enterprise environment - Explain the importance of physical security controls - Summarize authentication and authorization design concepts - Explain the security implications of embedded and specialized systems - Summarize virtualization and cloud security concepts - Given a scenario, implement cybersecurity resilience - Explain secure application development, deployment, and automation concepts - Summarize basics of cryptographic concepts |
| Implementation | 25% | - Given a scenario, implement authentication and authorization solutions - Given a scenario, implement secure mobile device policies - Given a scenario, implement secure systems design - Given a scenario, implement public key infrastructure (PKI) - Given a scenario, implement secure host settings - Given a scenario, apply cybersecurity solutions to the cloud - Given a scenario, implement identity and account management controls - Given a scenario, implement secure network architecture concepts |
| Operations and Incident Response | 16% | - Given a scenario, use data sources to support an investigation - Summarize the importance of policies, processes, and procedures for incident response - Explain key aspects of digital forensics - Given a scenario, apply mitigation techniques or controls to secure an environment - Given a scenario, use appropriate tool to assess organizational security |
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NEW QUESTION # 92
Which of the following technologies is used in deepfake?
Answer: A
Explanation:
Basic Concept: Deepfakes are AI-generated synthetic media that convincingly replace or manipulate a person
' s likeness, voice, or actions in images and videos. Creating realistic deepfakes requires generative AI techniques capable of learning and reproducing complex data distributions. CompTIA SecAI+ Exam Objectives cover deepfake technology under basic AI concepts.
Why A is Correct: Generative Adversarial Networks (GANs) are the primary technology behind deepfakes. A GAN consists of two competing neural networks: a generator that creates synthetic content and a discriminator that evaluates whether content is real or fake. Through adversarial training, the generator continuously improves at creating convincing synthetic media such as realistic human faces, voice clones, and video manipulations indistinguishable from authentic recordings.
Why B is Wrong: Multi-shot prompting is a prompting technique where multiple examples are provided to an LLM to guide its responses. It is an inference technique for language models and has no role in generating synthetic video or image deepfake content.
Why C is Wrong: Prompt engineering is the practice of crafting effective prompts to guide LLM outputs. It is a communication strategy for working with text-based AI systems, not a technology for generating synthetic media.
Why D is Wrong: Transfer learning is a training technique that repurposes knowledge from one domain to another, improving model performance with limited data. While it can be used in model training pipelines, it is not the core technology that enables deepfake generation.
NEW QUESTION # 93
A phishing attachment appears harmless during static analysis but behaves maliciously when executed. Which technique would detect this?
Answer: C
Explanation:
Dynamic analysis observes real-time malicious activity.
NEW QUESTION # 94
A SOC analyst identifies that a user extracted the full system prompt from the company ' s chatbot by prompting it to repeat the last query and provide the entire conversation context. Which of the following mitigations reduces the risk to the AI system?
Answer: C
Explanation:
Basic Concept: System prompt extraction is an attack where users manipulate an LLM into revealing its confidential system instructions. This violates the confidentiality of proprietary prompts and can expose security controls and business logic to adversaries. CompTIA SecAI+ Study Guide identifies guardrails as the primary control for preventing system prompt disclosure.
Why C is Correct: Enhancing model guardrails can specifically include instructions and filters that prevent the model from revealing its system prompt contents, regardless of how users attempt to extract them. Guardrails can detect and block attempts to retrieve conversation history, repeat system-level instructions, or disclose confidential operational context. This directly addresses the demonstrated attack where the user prompted the chatbot to reveal its entire context including the system prompt.
Why A is Wrong: Restricting the LLM ' s access to internal services limits what external resources the model can query. While this reduces the potential impact of system compromise, it does not prevent the model from disclosing its own system prompt in response to carefully crafted user queries.
Why B is Wrong: Data version control tracks changes to datasets and documents over time. It is a data management tool that does not inspect or control what the model discloses in its conversational responses to users.
Why D is Wrong: Segregating and identifying external content is relevant for preventing prompt injection from external data sources. It does not directly prevent a user from successfully prompting the model to reveal its own internal system instructions.
NEW QUESTION # 95
An AI-based human resources system screens resumes to select the most suitable candidate for an interview.
The system recommends mostly male candidates. Which of the following responsible AI concepts does this system violate?
Answer: B
NEW QUESTION # 96
A security analyst reviews a recently released chatbot ' s log and discovers that outputs sometimes include personally identifiable information (PII) from other chatbot users.
Which of the following corrective actions should the security analyst take first to resolve this issue?
Answer: D
Explanation:
Basic Concept: When a chatbot leaks PII from one user ' s conversation into another user ' s responses, the root cause is cross-user memory contamination - the chatbot is retaining and sharing conversation context across user sessions. Disabling the memory feature stops the active data leakage immediately. CompTIA SecAI+ Study Guide covers session memory management as a privacy control for AI chatbots.
Why B is Correct: Disabling memory from chat history for all users immediately stops the mechanism causing PII leakage between users. If the chatbot retains no cross-session memory, it cannot include information from one user ' s conversation in another user ' s response. This is the most direct, immediate corrective action that eliminates the root cause of the privacy violation without requiring additional user behavior changes or service disruption.
Why A is Wrong: Taking the chatbot offline and restoring from backup is a drastic action appropriate when the issue requires investigating a potential compromise or data breach. For a configuration issue such as cross- user memory sharing, disabling the memory feature is a more targeted and proportionate first response that addresses the root cause directly.
Why C is Wrong: Asking users to refrain from using PII relies on voluntary user behavior change and does not address the technical root cause. Users may not comply, and even if they do, previously stored PII in memory would continue to leak. This is an ineffective first corrective action.
Why D is Wrong: Requiring users to label sensitivity does not stop the chatbot from storing and sharing PII that has already been submitted. Labels inform the system about data sensitivity but do not prevent the memory mechanism from sharing labeled sensitive data across user sessions.
NEW QUESTION # 97
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