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| Section | Weight | Objectives |
|---|---|---|
| AI Governance, Risk, and Compliance | 19% | - Risk and Compliance
|
| AI-Assisted Security | 24% | - Security Operations Enhancement
|
| Securing AI Systems | 40% | - AI System Protection
|
| Basic AI Concepts Related to Cybersecurity | 17% | - AI and Machine Learning Fundamentals
|
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NEW QUESTION # 22
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: A
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 # 23
Which of the following corporate policies would a security architect most likely reference when configuring an AI system to conduct input filtering?
Answer: B
Explanation:
Data governance is the most relevant corporate policy because input filtering depends on knowing what information may enter the AI system, how that information is classified, and which categories of sensitive or regulated data must be blocked, redacted, masked, or otherwise controlled. CompTIA SecAI+ specifically includes sensitive data governance under corporate AI policies and separately identifies data classification labels, redaction, masking, minimization, and related data-safety controls. A security architect can therefore translate the organization ' s data governance rules into technical input-filtering requirements, such as preventing credentials, customer PII, financial information, confidential intellectual property, or restricted datasets from being submitted to a model. A public-model usage policy governs whether and how employees may use externally hosted models, but it is narrower than the underlying data-handling rules. Third-party compliance evaluates external providers and contractual or regulatory obligations. Terms of use describe permitted user behavior but normally do not provide the detailed data-classification requirements required to implement technical filtering. Data governance is therefore the authoritative basis for determining what input the AI system should accept or reject.
NEW QUESTION # 24
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: B
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 # 25
Instructions: Click the (+) to assign each threat category into its appropriate framework.
An architect is modeling an agentic system to meet security standards.
Answer:
Explanation:
See Explanation below for complete solution for this PBQ.
Explanation:
Basic Concept: This is a Performance-Based Question (PBQ) - a simulation item requiring interactive drag- and-drop assignment of threat categories to appropriate frameworks in the actual exam. It tests knowledge of how different AI threat frameworks categorize and address specific threat types for agentic systems.
Key Concept - Framework-to-Threat Mapping: MITRE ATLAS covers ML-specific adversarial tactics such as model evasion, data poisoning, model extraction, and prompt injection for agentic systems. OWASP LLM Top 10 addresses application-level LLM vulnerabilities such as insecure output handling, excessive agency, and supply chain risks. NIST AI RMF addresses governance-level risks across the AI lifecycle. STRIDE addresses architectural threats including spoofing, tampering, repudiation, information disclosure, DoS, and elevation of privilege.
Why This Matters: Agentic AI systems have a unique threat landscape combining traditional software vulnerabilities with AI-specific attacks. Correctly mapping threat categories to frameworks is essential for comprehensive threat modeling of systems that autonomously execute multi-step tasks with tool access and real-world consequences.
Reference: CompTIA SecAI+ Study Guide Domain 4 covers AI governance frameworks and their specific threat categories. Candidates should understand the scope and focus areas of MITRE ATLAS, OWASP LLM Top 10, NIST AI RMF, and traditional security frameworks as they apply to agentic AI system security modeling.
NEW QUESTION # 26
A financial services company is being sued for using AI in the loan underwriting process. The lawsuit reveals that the AI model used biased sources to disproportionately deny loans to residents in certain postal codes.
Which of the following employees is the most qualified to remove these biased sources from the model?
Answer: C
Explanation:
The data scientist is the most appropriate role because correcting biased source data requires examining the datasets used to develop, train, validate, or refine the model and then modifying the data preparation or model- development process. Data scientists work directly with training data, feature selection, statistical distributions, model development, and evaluation, which places them in the strongest position to identify biased inputs and retrain or adjust the model using more representative information. CompTIA SecAI+ includes the data scientist as a formal AI governance role and separately identifies fairness, introduction of bias, accuracy, and model performance as key responsible-AI risks. A platform engineer primarily manages the infrastructure supporting AI workloads. A cybersecurity analyst focuses on security threats, monitoring, and defensive controls rather than correcting discriminatory training data. An AI architect designs the broader architecture and integration of AI solutions but normally does not perform the detailed dataset preparation and model-training work required here. Because the problem originates in biased data sources, the data scientist is the role closest to the underlying cause.
NEW QUESTION # 27
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