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| Section | Weight | Objectives |
|---|---|---|
| Governance, Risk, and Compliance | 14% | - Explain risk management processes and concepts - 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 |
| Architecture and Design | 21% | - Explain secure application development, deployment, and automation concepts - Explain the importance of security concepts in an enterprise environment - Given a scenario, implement cybersecurity resilience - Explain the security implications of embedded and specialized systems - Summarize basics of cryptographic concepts - Summarize authentication and authorization design concepts - Summarize virtualization and cloud security concepts - Explain the importance of physical security controls |
| Operations and Incident Response | 16% | - Explain key aspects of digital forensics - Given a scenario, use appropriate tool to assess organizational security - Given a scenario, use data sources to support an investigation - Given a scenario, apply mitigation techniques or controls to secure an environment - Summarize the importance of policies, processes, and procedures for incident response |
| Implementation | 25% | - 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, implement secure systems design - Given a scenario, implement secure host settings - Given a scenario, implement secure network architecture concepts - Given a scenario, apply cybersecurity solutions to the cloud - Given a scenario, implement secure mobile device policies |
| Attacks, Threats, and Vulnerabilities | 24% | - Compare and contrast types of social engineering attacks - Given a scenario, analyze potential indicators to determine the type of attack - Explain penetration testing concepts - Given a scenario, analyze potential indicators associated with network attacks - Explain threat actor types and attributes - Given a scenario, analyze potential indicators associated with application attacks - Explain vulnerability scanning concepts |
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NEW QUESTION # 46
Which of the following describe the practice of providing examples in a prompt? (Choose two.)
Answer: A,F
Explanation:
Basic Concept: Prompting techniques for LLMs include various approaches to guide model behavior.
Providing examples within prompts is a powerful technique that leverages the model ' s in-context learning capability to guide response format and quality. CompTIA SecAI+ Study Guide covers prompting techniques under basic AI concepts.
Why E is Correct: One-shot prompting involves providing exactly one example within a prompt to demonstrate to the model the desired input-output format or response style. This single example guides the model ' s understanding of the task without requiring extensive fine-tuning. It is a well-established prompting technique that uses examples to inform model behavior.
Why F is Correct: Multi-shot prompting (also called few-shot prompting) involves providing multiple examples within a prompt to further clarify the desired output pattern. Multiple examples help the model identify consistent patterns and produce more accurate, consistent responses. Both one-shot and multi-shot are specifically defined by their use of examples in prompts.
Why A is Wrong: A user prompt is the input message submitted by a user to the AI system. It is the general term for any user input, not a specific technique that describes the practice of providing examples.
Why B is Wrong: A system prompt sets the model ' s behavior, persona, and constraints at the session level.
While a system prompt could contain examples, the term specifically refers to the system-level instruction context, not the technique of example provision.
Why C is Wrong: A prompt template is a reusable structured format with placeholders for variable inputs. It standardizes prompt structure but is not defined by the practice of including examples.
Why D is Wrong: Quantization is a model compression technique that reduces model size by representing weights with lower precision numbers. It is a model optimization technique completely unrelated to prompting practices.
NEW QUESTION # 47
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: C
Explanation:
The NIST AI RMF provides structured guidance for assessing and managing risks across the AI lifecycle, including posture management. It helps organizations align AI security practices with governance, resilience, and trustworthiness requirements.
NEW QUESTION # 48
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: B
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 # 49
An organization deploys an application programming interface (API) to allow external customers to perform tasks supported by internally developed AI models. Some customers require limited use of sensitive data.
After the API is deployed, customers report that the API returns sensitive data to all customers. Which of the following is the best action to take with the API?
Answer: B
Explanation:
Option D is correct because the failure is an authorization problem: every external customer can receive sensitive information even though only some customers are permitted to use it. Role-based access control assigns permissions to defined customer roles and requires the API to evaluate the caller's role before returning protected fields or invoking sensitive model functions. Properly implemented, RBAC supports least privilege and separates ordinary customers from approved sensitive-data users. Option A may route customers to different models, but model selection does not itself enforce who is authorized to receive data. Option B changes model behavior and training data, yet retraining cannot replace an access-control decision at the API boundary. Option C may improve network isolation, but placing the model in a VPC does not stop an authenticated external customer from receiving information that the API exposes. The organization should also apply field-level filtering, deny-by-default policies, logging, and authorization testing. NIST's RBAC model assigns users to roles and associates each role with permitted privileges, directly supporting this control.
NEW QUESTION # 50
A security analyst receives an alert about an AI system and is investigating the following output:
Which of the following is the most appropriate control the analyst should recommend?
Answer: B
Explanation:
Basic Concept: Suspicious or unexpected AI system outputs are often caused by malicious or malformed user inputs that exploit the AI ' s input processing. Validating and sanitizing user inputs before they reach the AI model prevents many classes of attacks including prompt injection, data exfiltration attempts, and input manipulation. CompTIA SecAI+ Study Guide emphasizes input validation as a foundational AI security control.
Why B is Correct: Implementing user input validation applies checks and constraints to all user-submitted content before it is processed by the AI system. Input validation can enforce length limits, detect injection patterns, filter disallowed characters or command structures, and ensure inputs conform to expected formats.
By rejecting malicious or malformed inputs at the entry point, this control prevents them from reaching the model and causing the suspicious outputs observed.
Why A is Wrong: Data sanitization processes data to remove harmful elements and is closely related to validation. However, input validation is broader and more proactive, checking conformance to rules before processing, while sanitization typically operates during or after processing. Validation at the input boundary is the more appropriate first-line control.
Why C is Wrong: Monitoring logs for attack keywords is a detective control that identifies attacks after they have already affected the system. It does not prevent suspicious outputs from being generated in the first place.
Why D is Wrong: Hardening the Model Context Protocol server improves the security of the infrastructure hosting the AI components. While important for infrastructure security, it does not directly validate or inspect the content of user inputs that cause suspicious outputs.
NEW QUESTION # 51
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