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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Governance, Risk, and Compliance | 14% | - Given a scenario, follow organizational security policies and procedures - Summarize regulations, standards, and frameworks that impact organizations - Explain risk management processes and concepts - Explain privacy and sensitive data concepts in relation to security - Compare and contrast various types of security controls |
| Topic 2: 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 - Summarize the importance of policies, processes, and procedures for incident response - Given a scenario, apply mitigation techniques or controls to secure an environment |
| Topic 3: Architecture and Design | 21% | - Summarize basics of cryptographic concepts - 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 authentication and authorization design concepts - Given a scenario, implement cybersecurity resilience - Explain the security implications of embedded and specialized systems |
| Topic 4: Implementation | 25% | - Given a scenario, implement secure network architecture concepts - Given a scenario, implement secure systems design - Given a scenario, implement identity and account management controls - Given a scenario, implement secure mobile device policies - Given a scenario, implement authentication and authorization solutions - Given a scenario, implement secure host settings - Given a scenario, implement public key infrastructure (PKI) - Given a scenario, apply cybersecurity solutions to the cloud |
| Topic 5: Attacks, Threats, and Vulnerabilities | 24% | - Given a scenario, analyze potential indicators associated with application attacks - 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 to determine the type of attack - Explain threat actor types and attributes - Explain vulnerability scanning concepts |
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NEW QUESTION # 142
Which of the following job roles in an organizational governance structure develops a model from business use cases?
Answer: D
Explanation:
Basic Concept: In AI governance, each role holds distinct responsibilities. Understanding these roles is core to CompTIA SecAI+ Domain 4 (AI Governance, Risk, and Compliance).
Why D is Correct: The Data Scientist is responsible for translating business use cases into working AI/ML models. They analyze business requirements, identify the appropriate machine learning approach, and develop models that fulfill specific business objectives. According to the CompTIA SecAI+ Study Guide, data scientists bridge raw data and actionable AI solutions by building and validating models derived from business-driven needs.
Why A is Wrong: A Platform Architect designs and manages the infrastructure and technical platforms hosting AI systems. Their focus is architectural design of the environment, not model development from business use cases.
Why B is Wrong: An AI Risk Analyst identifies, evaluates, and mitigates risks associated with AI adoption.
Their role is governance and risk-oriented, not model creation.
Why C is Wrong: An MLOps Engineer operationalizes, deploys, monitors, and maintains AI models in production. They take models already built by data scientists and ensure reliable operation at scale, not develop them from business use cases.
NEW QUESTION # 143
Customer feedback for an AI chatbot has a high-rate of non-answers, which is causing higher central processing unit (CPU) utilization.
Which of the following should be implemented?
Answer: A
Explanation:
Basic Concept: AI chatbots that generate non-answers - responses that do not actually address user questions
- consume CPU resources for processing without delivering value. This can indicate the model is attempting to generate responses for queries outside its knowledge domain or confidence threshold. CompTIA SecAI+ Study Guide covers AI performance optimization and response quality management.
Why B is Correct: Implementing a response confidence level threshold allows the chatbot to recognize when it lacks sufficient confidence to provide a meaningful answer and respond accordingly, either with a helpful redirect or a clear indication that it cannot answer the query. This reduces the costly processing cycles spent generating poor-quality non-answers, lowers CPU utilization from failed response generation, and improves customer experience by setting appropriate expectations rather than returning unhelpful responses.
Why A is Wrong: Guardrails filter content for safety and policy compliance. They prevent harmful or out-of- policy responses but do not address the underlying issue of the model generating low-confidence non-answers to legitimate customer queries.
Why C is Wrong: Prompt logging records user inputs for analysis and auditing. While useful for identifying what types of questions cause non-answers, logging alone does not solve the problem or reduce CPU utilization from failed response generation.
Why D is Wrong: Cost monitoring tracks AI system expenditure. It can identify that costs are high due to excessive CPU usage but does not implement a solution to reduce the non-answer rate or improve response generation efficiency.
NEW QUESTION # 144
An AI security administrator receives an inquiry about an unusually high monthly bill from the AI solution provider. The administrator thinks the majority of staff might be using the most powerful model available.
Which of the following AI measures should the administrator implement to lower costs?
Answer: C
Explanation:
Basic Concept: LLM API billing is primarily based on token consumption. High costs resulting from staff using powerful, verbose models can be controlled by limiting the maximum tokens processed per interaction and restricting which models staff can access. CompTIA SecAI+ Study Guide covers token management as the primary cost control mechanism for AI deployments.
Why D is Correct: Implementing token limits caps the maximum tokens consumed per API call for both input and output. This directly controls the per-interaction cost by preventing excessively long prompts or overly verbose model responses from generating large token bills. Combined with model tier restrictions, token limits ensure that interactions with powerful models remain within budget constraints regardless of how extensively staff use the service.
Why A is Wrong: Storage monitoring tracks the utilization and performance of data storage systems. Storage costs are separate from LLM API token-based billing and monitoring storage does not address the high API charges resulting from excessive model usage by staff.
Why B is Wrong: Modality types refer to the input formats an AI model accepts such as text, images, audio, or video. While different modalities have different pricing, managing modality types is not the direct cost control lever. Token consumption is the primary cost driver for text-based interactions.
Why C is Wrong: Prompt firewalls inspect and filter prompt content for security and policy compliance.
While they can block certain types of queries, they are designed for security purposes, not as financial controls to limit token consumption or enforce cost budgets across staff usage.
NEW QUESTION # 145
A security analyst is aware of an active penetration test in the environment. The analyst examines security information and event management (SIEM) log data and notices the following output from the AI system:
Which of the following is the vulnerability that has occurred and the control the analyst should implement?
Answer: E
Explanation:
The log data reveals personally identifiable information (PII) such as name, address, and a full credit card number. This represents a sensitive information disclosure vulnerability. The appropriate control is data masking, which protects sensitive data in logs and outputs while still allowing necessary system monitoring.
NEW QUESTION # 146
An internal user enters a client credit card number into an internal generative machine learning (ML) model:
#User prompt: Customer Jane Doe has a new credit card that she wants to add to her account. The number is
5555-5555-5555-5555
Which of the following is the most effective way to prevent prompt injection attacks against a large language model (LLM)?
Answer: D
Explanation:
Basic Concept: Prompt injection occurs when malicious content embedded in user input manipulates an LLM
' s behavior, causing it to leak sensitive data, bypass restrictions, or execute unintended actions. Preventing such attacks requires mechanisms that inspect and filter content at the prompt level. CompTIA SecAI+ covers LLM-specific security controls extensively.
Why A is Correct: Guardrails are purpose-built controls that inspect, filter, and constrain both input prompts and output responses in LLM systems. They can detect sensitive data patterns such as credit card numbers, block prompt injection payloads, enforce content policies, and prevent the model from processing or outputting restricted information. Guardrails are the primary LLM-native defense against prompt injection as cited in the CompTIA SecAI+ Study Guide.
Why B is Wrong: Antivirus software detects known malware signatures in files and executables. It does not inspect or understand the semantic content of LLM prompts and cannot detect or block prompt injection attacks.
Why C is Wrong: A WAF operates at the HTTP layer inspecting web requests and responses against rule sets.
While it can block some patterns, it lacks the contextual intelligence to understand LLM prompt semantics and cannot prevent sophisticated injection attacks.
Why D is Wrong: Role-based access control manages who can access which resources. It controls authorization but does not inspect the content of prompts to prevent injection attacks once a user has legitimate access.
NEW QUESTION # 147
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