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| Section | Weight | Objectives |
|---|---|---|
| Implementation | 25% | - Given a scenario, implement secure network architecture concepts - Given a scenario, implement secure mobile device policies - Given a scenario, implement authentication and authorization solutions - Given a scenario, implement identity and account management controls - Given a scenario, implement secure host settings - Given a scenario, implement secure systems design - Given a scenario, apply cybersecurity solutions to the cloud - Given a scenario, implement public key infrastructure (PKI) |
| Operations and Incident Response | 16% | - Given a scenario, apply mitigation techniques or controls to secure an environment - Explain key aspects of digital forensics - Summarize the importance of policies, processes, and procedures for incident response - Given a scenario, use appropriate tool to assess organizational security - Given a scenario, use data sources to support an investigation |
| Attacks, Threats, and Vulnerabilities | 24% | - Explain vulnerability scanning concepts - Explain penetration testing concepts - Given a scenario, analyze potential indicators to determine the type of attack - Compare and contrast types of social engineering attacks - Given a scenario, analyze potential indicators associated with application attacks - Given a scenario, analyze potential indicators associated with network attacks - Explain threat actor types and attributes |
| Governance, Risk, and Compliance | 14% | - 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 - Summarize regulations, standards, and frameworks that impact organizations |
| Architecture and Design | 21% | - Given a scenario, implement cybersecurity resilience - Explain the importance of physical security controls - Summarize virtualization and cloud security concepts - Explain the importance of security concepts in an enterprise environment - Explain secure application development, deployment, and automation concepts - Summarize basics of cryptographic concepts - Summarize authentication and authorization design concepts - Explain the security implications of embedded and specialized systems |
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NEW QUESTION # 58
A healthcare company deploys an AI chatbot that implements retrieval-augmented generation (RAG) using the company ' s historical data set. The chatbot output contains patient information.
Which of the following is the most effective technique to mitigate this vulnerability?
Answer: D
Explanation:
Basic Concept: When an AI chatbot powered by RAG retrieves and outputs sensitive patient information such as names, medical histories, or identifiers, the risk of Protected Health Information (PHI) disclosure must be mitigated. CompTIA SecAI+ Study Guide covers data protection techniques for AI systems handling sensitive health data.
Why A is Correct: Masking replaces sensitive data values such as patient names, dates of birth, medical record numbers, and diagnoses with redacted or anonymized equivalents in the chatbot ' s output. Even if the RAG system retrieves records containing patient information, masking ensures that the sensitive fields are obscured before the response is presented to the user. This directly prevents PHI disclosure while allowing the chatbot to provide useful responses based on the underlying data patterns.
Why B is Wrong: Classification involves categorizing data by its sensitivity level such as public, internal, confidential, or restricted. While it identifies which data requires protection, classification alone does not transform or obscure the sensitive values in chatbot outputs.
Why C is Wrong: Data minimization is a privacy principle that limits data collection to only what is necessary for the specified purpose. While valuable as a design principle when building the RAG knowledge base, it is a data governance strategy rather than a technical output control that can be applied to mitigate existing PHI disclosure in chatbot responses.
Why D is Wrong: Normalization is a data processing technique that scales numerical values to a standard range or standardizes data formats. It is a preprocessing step for improving model training efficiency, not a data protection technique for preventing patient information disclosure in AI outputs.
NEW QUESTION # 59
A security administrator must implement security controls for AI systems.
Which of the following access controls should the administrator set up first for authentication?
Answer: B
Explanation:
Basic Concept: In a layered AI system security architecture, access control must be established at each layer, beginning from the outermost point of entry. Authentication must be established at the endpoint level first, as this is the first point of interaction between users and the AI system. CompTIA SecAI+ Study Guide establishes endpoint authentication as the initial access control layer for AI systems.
Why D is Correct: Endpoint access control is the first authentication control to implement because it governs the initial connection from user devices or client applications to the AI system. All subsequent access layers including server access, model access, and data access depend on the endpoint being authenticated first.
Establishing endpoint authentication ensures that only authorized endpoints can initiate sessions and proceed through subsequent authentication layers.
Why A is Wrong: Model access controls govern who can query, update, or access the AI model ' s parameters and functions. This control layer is implemented after endpoint authentication has been established, as it applies to requests that have already been authenticated at the endpoint level.
Why B is Wrong: Server access controls manage access to the computing infrastructure hosting the AI system. While critical for infrastructure security, server-level controls are configured by administrators and are not the first authentication control for end-user access flows.
Why C is Wrong: Data access controls define what data the AI system and its users can read, write, or query.
These are implemented at a deeper layer after endpoint and potentially model authentication have verified that the requester is authorized to interact with the system at all.
NEW QUESTION # 60
What control reduces the impact radius when a single host is compromised?
Answer: D
Explanation:
Segmentation isolates systems and limits lateral movement.
NEW QUESTION # 61
Which of the following describe the practice of providing examples in a prompt? (Choose two.)
Answer: A,C
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 # 62
Which of the following International Organization for Standardization (ISO) standards should be selected for certification to use for third-party assurance for responsible AI practices?
Answer: D
Explanation:
Basic Concept: ISO develops international standards for management systems across various domains. For organizations seeking third-party certification demonstrating commitment to responsible AI governance practices, the appropriate ISO standard must specifically address AI management systems. CompTIA SecAI+ Exam Objectives cover ISO standards relevant to AI governance under Domain 4.
Why D is Correct: ISO 42001 is the International Standard for Artificial Intelligence Management Systems (AIMS). It provides a framework for establishing, implementing, maintaining, and continually improving an AI management system within organizations. ISO 42001 certification provides third-party assurance specifically for responsible AI practices including risk management, transparency, human oversight, and ethical AI governance - directly answering the question.
Why A is Wrong: ISO 20000 is the standard for IT Service Management (ITSM). It provides requirements for establishing and maintaining a service management system for IT services. It does not address AI governance or responsible AI practices.
Why B is Wrong: ISO 27001 is the standard for Information Security Management Systems (ISMS). It addresses general information security risk management, not AI-specific governance or responsible AI practices such as fairness, transparency, and AI lifecycle management.
Why C is Wrong: ISO 27701 extends ISO 27001 to address Privacy Information Management (PIMS), covering personal data protection requirements aligned with GDPR. While relevant to data privacy in AI systems, it does not specifically certify responsible AI governance practices.
NEW QUESTION # 63
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