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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Architecture and Design | 21% | - Summarize virtualization and cloud security concepts - Explain secure application development, deployment, and automation concepts - Summarize authentication and authorization design concepts - Explain the importance of physical security controls - Given a scenario, implement cybersecurity resilience - Explain the importance of security concepts in an enterprise environment - Summarize basics of cryptographic concepts - Explain the security implications of embedded and specialized systems |
| Topic 2: Governance, Risk, and Compliance | 14% | - Explain risk management processes and concepts - Compare and contrast various types of security controls - Explain privacy and sensitive data concepts in relation to security - Given a scenario, follow organizational security policies and procedures - Summarize regulations, standards, and frameworks that impact organizations |
| Topic 3: Implementation | 25% | - Given a scenario, implement secure mobile device policies - Given a scenario, implement secure host settings - Given a scenario, implement authentication and authorization solutions - Given a scenario, implement public key infrastructure (PKI) - Given a scenario, implement identity and account management controls - Given a scenario, implement secure network architecture concepts - Given a scenario, implement secure systems design - Given a scenario, apply cybersecurity solutions to the cloud |
| Topic 4: Attacks, Threats, and Vulnerabilities | 24% | - Explain vulnerability scanning concepts - Given a scenario, analyze potential indicators to determine the type of attack - Compare and contrast types of social engineering attacks - Explain penetration testing concepts - Given a scenario, analyze potential indicators associated with network attacks - Given a scenario, analyze potential indicators associated with application attacks - Explain threat actor types and attributes |
| Topic 5: Operations and Incident Response | 16% | - Given a scenario, use appropriate tool to assess organizational security - 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 |
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NEW QUESTION # 109
Which of the following attacks would be the best to automate with AI during dynamic application software testing (DAST)?
Answer: C
Explanation:
During DAST, automating the generation of diverse, targeted attack payloads lets testers probe runtime inputs (e.g., XSS, SQLi, command injection) more thoroughly and discover vulnerabilities that manual or static tests might miss.
NEW QUESTION # 110
Which of the following resources most accurately covers the most widely recognized threat vectors and common vulnerabilities related to large language model (LLM) applications?
Answer: C
Explanation:
OWASP is the best answer because it publishes the Top 10 for Large Language Model Applications, a resource specifically structured around significant security vulnerabilities and attack patterns affecting LLM- based applications. CompTIA SecAI+ Objective 2.1 directly lists the OWASP Top 10 and specifically the LLM Top 10 as an AI threat-modeling resource. MITRE ATLAS is also highly relevant to AI security, but its purpose is broader: it catalogs adversarial tactics, techniques, and procedures associated with attacks against AI and machine learning systems. The MIT AI Risk Repository aggregates a broad range of AI risks rather than functioning as the recognized LLM application vulnerability Top 10. NIST AI RMF provides a risk- management framework for governing and managing AI risks but is not primarily a vulnerability catalog for LLM applications. Since the question asks for the resource that most directly captures widely recognized threat vectors and common vulnerabilities associated specifically with LLM applications, OWASP ' s LLM- focused Top 10 is the most precise match.
NEW QUESTION # 111
A company introduces a large language model (LLM) in an application in order to monitor for a potential denial-of-service attack.
Which of the following should the company use to measure the utilization of the LLM?
Answer: A
Explanation:
Basic Concept: LLMs process and generate text as discrete units called tokens, which represent words or word pieces. The number of tokens consumed is the primary metric for LLM resource utilization and billing.
Understanding token consumption is essential for both performance monitoring and cost management.
CompTIA SecAI+ Study Guide covers token-based measurement under AI system management.
Why A is Correct: Tokens are the fundamental unit of measurement for LLM utilization. Both input tokens sent in prompts and output tokens generated in responses are counted. Token consumption directly correlates with computational resource usage, response time, and API cost. Monitoring token counts enables the company to establish usage baselines and detect the abnormal token consumption spikes associated with DoS attacks or denial-of-wallet attacks against the LLM.
Why B is Wrong: A transformer is the neural network architecture that underlies modern LLMs. It is an architectural description, not a measurable utilization metric. You cannot quantify LLM resource consumption in " transformers. " Why C is Wrong: Chain of thought is a prompting technique that encourages models to reason step-by-step before providing answers. It is an inference strategy for improving response quality, not a unit of measurement for monitoring LLM utilization or resource consumption.
Why D is Wrong: A prompt is the input text submitted to the LLM. While monitoring prompt volumes provides request count information, tokens provide a more granular and meaningful utilization metric because they capture both the complexity of inputs and the length of generated outputs.
NEW QUESTION # 112
A social media company with more than a million lines of code wants to reduce the mean time to fix bugs and issues. Which of the following is the most balanced AI strategy to automate the vulnerability management flow?
Answer: B
Explanation:
This approach balances automation and human oversight. AI accelerates vulnerability management by triaging issues and generating tickets, while software engineers retain responsibility for merging code changes, ensuring quality and reducing the risk of insecure or unstable code being deployed.
NEW QUESTION # 113
Which of the following describes the most significant risk associated with AI agents that make autonomous decisions?
Answer: B
Explanation:
Output bias is the best answer because an autonomous AI agent can convert biased model judgments into consequential decisions without requiring a human to review each result. CompTIA SecAI+ identifies introduction of bias as a significant AI risk and separately identifies autonomous systems as an important governance concern. Responsible AI principles such as fairness, reliability, accountability, explainability, and human oversight exist partly to control this problem.
If an agent ' s model produces systematically biased recommendations, those biased outputs can directly influence hiring, access decisions, financial transactions, security actions, or other automated processes.
Autonomy therefore increases the impact of output bias because an erroneous or unfair recommendation may become an action before a person can intervene.
Accidental data leakage is also an important AI risk, but the question emphasizes autonomous decision- making , making biased decisions the more directly associated concern. Overfitting is primarily a model- development problem in which a model memorizes training characteristics and fails to generalize effectively.
Increased workload is contrary to one of the primary motivations for using agents, which is automation.
Therefore, output bias represents the most relevant risk among the available choices.
NEW QUESTION # 114
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