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| Section | Weight | Objectives |
|---|---|---|
| Operations and Incident Response | 16% | - Given a scenario, use data sources to support an investigation - Given a scenario, use appropriate tool to assess organizational security - Explain key aspects of digital forensics - Given a scenario, apply mitigation techniques or controls to secure an environment - Summarize the importance of policies, processes, and procedures for incident response |
| Governance, Risk, and Compliance | 14% | - Explain risk management processes and concepts - Summarize regulations, standards, and frameworks that impact organizations - 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 |
| Architecture and Design | 21% | - Explain the importance of physical security controls - Summarize virtualization and cloud security concepts - Explain the security implications of embedded and specialized systems - Summarize authentication and authorization design concepts - Explain the importance of security concepts in an enterprise environment - 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 secure mobile device policies - 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 identity and account management controls - Given a scenario, implement public key infrastructure (PKI) - Given a scenario, apply cybersecurity solutions to the cloud |
| Attacks, Threats, and Vulnerabilities | 24% | - Explain threat actor types and attributes - Explain penetration testing concepts - Given a scenario, analyze potential indicators associated with network attacks - Given a scenario, analyze potential indicators associated with application attacks - Compare and contrast types of social engineering attacks - Explain vulnerability scanning concepts - Given a scenario, analyze potential indicators to determine the type of attack |
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NEW QUESTION # 153
An attacker successfully completes a denial-of-service (DoS) attack through the context window of an AI system. Thousands of characters are obfuscated and hidden behind an emoji. Which of the following techniques best mitigates this type of attack?
Answer: B
Explanation:
A DoS attack through the context window relies on overwhelming the model with excessive or obfuscated input. Prompt filtering prevents such malicious or oversized inputs from being processed, ensuring that the model only receives safe, properly structured data within acceptable limits.
NEW QUESTION # 154
Which of the following improves the observability and auditing of an AI system?
Answer: A
Explanation:
MLOps provides structured monitoring, logging, and version control for AI models. This improves observability and ensures detailed auditing of model behavior, data pipelines, and changes throughout the AI lifecycle.
NEW QUESTION # 155
A company develops an AI model to diagnose patients. Hospitals access the model through an integrated application programming interface (API). The security team performs a denial-of-service (DoS) attack via brute force on the model.
Which of the following controls would have prevented this issue?
Answer: C
Explanation:
Basic Concept: API-based AI systems are susceptible to DoS attacks where excessive requests overwhelm the system ' s ability to respond to legitimate users. Rate limiting is the standard control for preventing both intentional and unintentional API abuse. CompTIA SecAI+ Study Guide covers rate limiting as a key availability control for AI APIs.
Why C is Correct: Rate limiting restricts the number of requests a client can make to an API within a defined time window. In this scenario, a brute-force DoS attack works by sending a massive volume of requests to exhaust the model ' s resources. Rate limiting would have automatically throttled or blocked the excessive request volume, preventing the attack from succeeding and preserving service availability for legitimate hospital users.
Why A is Wrong: Tokenization replaces sensitive data values with non-sensitive placeholders. It is a data security control for protecting sensitive information such as patient identifiers, not a control for managing API request volumes or preventing DoS attacks.
Why B is Wrong: Model guardrails filter and constrain model inputs and outputs for safety and policy compliance. They inspect content quality, not request volume, and cannot prevent a volume-based DoS attack.
Why D is Wrong: A prompt firewall inspects the content of prompts for malicious patterns or policy violations. Like guardrails, it analyzes content rather than controlling request frequency and cannot prevent resource exhaustion from a high-volume brute-force attack.
NEW QUESTION # 156
A company wants to detect abnormal insider activity based on historical logs. Which technology is BEST?
Answer: D
Explanation:
User and Entity Behavior Analytics detects deviations from normal patterns.
NEW QUESTION # 157
A management team is concerned about an unexpected cost increase for a public-facing AI chatbot.
Which of the following should a security administrator examine first to determine the root cause?
Answer: C
Explanation:
Basic Concept: AI chatbot operational costs are primarily driven by token consumption - the number of tokens processed in requests and generated in responses. Unexpected cost increases in LLM-based chatbots almost always trace back to abnormal token usage patterns. CompTIA SecAI+ Study Guide covers AI cost monitoring and token-based billing under securing AI systems.
Why D is Correct: Model token usage logs directly show how many tokens are being consumed per request, by which users or endpoints, and whether usage has increased abnormally. Examining token usage data is the most direct path to identifying the root cause of unexpected cost increases - whether from a denial-of-wallet attack, user abuse, a new feature generating verbose responses, or legitimate organic growth in usage. This is the first and most relevant examination point for LLM cost analysis.
Why A is Wrong: Firewall logs capture network-level traffic information. While they can reveal unusual access patterns or volumes, they do not contain token consumption data that directly explains LLM billing increases.
Why B is Wrong: WAF rules define filtering policies for web traffic. Reviewing rule configurations does not reveal whether token usage has increased or why costs have risen; it shows security policy settings rather than consumption metrics.
Why C is Wrong: Vector database IOPS performance measures how quickly the database processes read and write operations. While relevant to RAG system performance, IOPS metrics do not directly explain LLM API cost increases driven by token consumption.
NEW QUESTION # 158
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