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| Section | Weight | Objectives |
|---|---|---|
| Implementation | 25% | - 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 secure host settings - Given a scenario, implement secure mobile device policies - Given a scenario, implement secure network architecture concepts - Given a scenario, implement identity and account management controls - Given a scenario, implement public key infrastructure (PKI) |
| Architecture and Design | 21% | - Explain the importance of security concepts in an enterprise environment - Given a scenario, implement cybersecurity resilience - Explain the importance of physical security controls - Summarize virtualization and cloud security concepts - Summarize authentication and authorization design concepts - Explain secure application development, deployment, and automation concepts - Explain the security implications of embedded and specialized systems - Summarize basics of cryptographic concepts |
| Attacks, Threats, and Vulnerabilities | 24% | - Given a scenario, analyze potential indicators associated with application attacks - Explain penetration testing concepts - Compare and contrast types of social engineering attacks - Given a scenario, analyze potential indicators associated with network attacks - Given a scenario, analyze potential indicators to determine the type of attack - Explain vulnerability scanning concepts - Explain threat actor types and attributes |
| Operations and Incident Response | 16% | - Summarize the importance of policies, processes, and procedures for incident response - Given a scenario, apply mitigation techniques or controls to secure an environment - 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 |
| Governance, Risk, and Compliance | 14% | - Summarize regulations, standards, and frameworks that impact organizations - 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 - Explain risk management processes and concepts |
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NEW QUESTION # 67
Which of the following would most likely be used to prove that an image is AI generated?
Answer: D
Explanation:
Basic Concept: As AI-generated images become increasingly indistinguishable from authentic photographs, technical mechanisms are needed to definitively identify synthetic content. Embedding verifiable markers in AI-generated content at creation time provides a reliable provenance trail. CompTIA SecAI+ Study Guide covers digital content authentication and AI provenance controls.
Why D is Correct: Watermarking embeds invisible or visible identifying markers into AI-generated images at the point of creation. These watermarks can be cryptographic, steganographic, or perceptual in nature and survive compression, cropping, and other common image manipulations. When an image ' s provenance is questioned, watermark detection tools can analyze it to confirm AI generation. This is the primary technical mechanism for proving AI image origin as supported by the Content Authenticity Initiative and C2PA standards.
Why A is Wrong: Human validation relies on subjective visual inspection by experts to determine if an image appears AI-generated. Modern AI image generation has advanced to the point where human reviewers frequently cannot distinguish synthetic from authentic images, making this approach unreliable for definitive proof.
Why B is Wrong: Guardrails are input and output filtering controls for AI systems. They restrict what content an AI can generate but do not create verifiable proof of an image ' s AI origin after generation.
Why C is Wrong: Diffusion refers to the diffusion model architecture used to generate images. It describes the creation process, not a verification mechanism. Knowing that diffusion models were used does not help prove a specific image was AI-generated after the fact.
NEW QUESTION # 68
A security consultant must summarize the impact of posture management on a machine learning (ML) use case.
Which of the following is the most appropriate reference for this purpose?
Answer: A
Explanation:
Basic Concept: Security posture management for AI systems involves assessing and improving the overall security state of AI deployments, including identifying risks, implementing controls, and maintaining ongoing compliance. Appropriate frameworks provide structure for this assessment. CompTIA SecAI+ Study Guide identifies NIST AI RMF as the primary framework for AI risk and posture management.
Why B is Correct: The NIST AI Risk Management Framework provides comprehensive, actionable guidance for managing and improving AI security and risk posture across the entire AI lifecycle. It includes the GOVERN, MAP, MEASURE, and MANAGE functions that directly address posture management activities including risk identification, assessment, and control implementation for ML use cases. Its technical depth and ML-specific guidance make it ideal for this summarization task.
Why A is Wrong: OECD standards provide high-level policy principles for AI governance at an international level. They lack the technical specificity and operational guidance needed to summarize posture management impact on a specific ML use case.
Why C is Wrong: The EU AI Act is a regulatory compliance framework establishing legal requirements for AI systems. While it addresses risk management, its focus is on legal compliance rather than technical posture management guidance for ML systems.
Why D is Wrong: A Generative Adversarial Network is an AI architecture for generating synthetic data, not a framework or standard. It has no relevance as a reference for AI security posture management.
NEW QUESTION # 69
A user interface engineer adds new graphics to the latest release of an AI-integrated application.
During the update, the engineer accidentally causes the model to retain on unverified data. After the update, the model begins to return many errors. Which of the following is the best way to mitigate future errors?
Answer: D
Explanation:
Following a secure and structured model development life cycle (MDLC) ensures controls such as data validation, verification, and testing are in place. This prevents issues like retraining on unverified data and reduces the likelihood of future model errors.
NEW QUESTION # 70
A cybersecurity administrator generates patching reports using AI, but the process takes a long time. Which of the following is the best way to increase performance?
Answer: D
Explanation:
Basic Concept: AI systems that repeatedly query external data sources for similar information during a single report generation process spend significant time on redundant network requests. Caching frequently accessed data locally eliminates this overhead. CompTIA SecAI+ Study Guide covers AI performance optimization strategies in security operations contexts.
Why B is Correct: Downloading the full CVE database locally before starting the cross-referencing process eliminates the need for multiple individual external API calls as the AI processes each OS version ' s patch list. Instead of making thousands of small external queries to look up CVE information for each patch-OS combination, the AI can query the locally cached database internally. This transforms multiple slow external network operations into fast local lookups, dramatically reducing report generation time.
Why A is Wrong: Using an MCP server to run multiple LLM queries simultaneously could improve throughput through parallelization. However, the fundamental bottleneck is external CVE database queries, not LLM processing capacity. Parallelizing LLM calls does not eliminate the external query latency.
Why C is Wrong: Specifying summarization algorithms in the system prompt affects how the AI structures its output. It does not address the time-consuming external data retrieval process that is the actual performance bottleneck in this cross-referencing workflow.
Why D is Wrong: Increasing token limits prevents session restarts for long contexts but does not address the external query latency that makes the report slow to generate. The bottleneck is data retrieval speed, not token limit constraints causing session breaks.
NEW QUESTION # 71
Which control BEST prevents attackers from harvesting password hashes during lateral movement?
Answer: A
Explanation:
LSASS-protection solutions prevent hash extraction.
NEW QUESTION # 72
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