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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Implementation | 25% | - Given a scenario, implement identity and account management controls - Given a scenario, implement public key infrastructure (PKI) - 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 systems design - Given a scenario, implement authentication and authorization solutions - Given a scenario, implement secure mobile device policies |
| Topic 2: Attacks, Threats, and Vulnerabilities | 24% | - Given a scenario, analyze potential indicators to determine the type of attack - Given a scenario, analyze potential indicators associated with application attacks - Explain threat actor types and attributes - Explain penetration testing concepts - Compare and contrast types of social engineering attacks - Given a scenario, analyze potential indicators associated with network attacks - Explain vulnerability scanning concepts |
| Topic 3: Governance, Risk, and Compliance | 14% | - 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 - Explain risk management processes and concepts |
| Topic 4: Operations and Incident Response | 16% | - Explain key aspects of digital forensics - Given a scenario, apply mitigation techniques or controls to secure an environment - Given a scenario, use data sources to support an investigation - Summarize the importance of policies, processes, and procedures for incident response - Given a scenario, use appropriate tool to assess organizational security |
| Topic 5: Architecture and Design | 21% | - Summarize authentication and authorization design concepts - Explain the security implications of embedded and specialized systems - Explain the importance of physical security controls - Summarize virtualization and cloud security concepts - Summarize basics of cryptographic concepts - Explain secure application development, deployment, and automation concepts - Given a scenario, implement cybersecurity resilience - Explain the importance of security concepts in an enterprise environment |
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NEW QUESTION # 49
An organization recently created a custom model that integrates with a language model (LLM).
The developer notices that the application programming interface (API) costs have increased.
Which of the following is the best control to reduce cost?
Answer: C
Explanation:
API costs for large language model integrations are directly tied to token usage (input + output tokens). By adjusting token limits, the organization can reduce unnecessary processing of overly long prompts or responses, thereby lowering overall API costs without changing model size or infrastructure resources.
NEW QUESTION # 50
A multinational company wants to implement an AI-assisted job screening solution.
Which of the following should the company reference to reduce the risk of incurring compliance-related fines?
Answer: B
Explanation:
Basic Concept: AI systems used in employment contexts such as job screening carry significant regulatory risk. For a multinational company operating in or serving markets covered by the EU AI Act, compliance with this binding regulation is mandatory to avoid substantial fines. CompTIA SecAI+ Exam Objectives cover AI regulatory compliance under Domain 4.
Why B is Correct: The EU AI Act explicitly classifies AI systems used for employment screening, candidate evaluation, and worker management as high-risk AI applications. These systems are subject to strict compliance requirements including mandatory conformity assessments, human oversight, transparency obligations, and registration. Non-compliance can result in fines up to 30 million euros or 6% of global annual turnover. A multinational company implementing AI job screening must reference the EU AI Act as the primary compliance obligation.
Why A is Wrong: ISO AI standards such as ISO 42001 are voluntary management system standards. While useful for best practices, they do not carry legal enforcement power and adherence does not prevent regulatory fines from binding legislation like the EU AI Act.
Why C is Wrong: Corporate policy is an internal governance document that sets organizational standards. It cannot supersede external legal obligations and following only corporate policy does not protect against fines from regulatory bodies enforcing the EU AI Act.
Why D is Wrong: NIST AI RMF is a voluntary American risk management framework. While excellent for AI risk governance, it is not a binding regulation and does not address the legal compliance requirements that generate fines from regulatory authorities in jurisdictions covered by the EU AI Act.
NEW QUESTION # 51
Which of the following job roles in an organizational governance structure develops a model from business use cases?
Answer: B
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 # 52
An organization recently created a custom model that integrates with a language model (LLM). The developer notices that the application programming interface (API) costs have increased.
Which of the following is the best control to reduce cost?
Answer: C
Explanation:
Basic Concept: LLM API pricing is primarily based on token consumption - the number of tokens processed in both input prompts and output responses. Controlling token usage is the most direct lever for managing and reducing LLM API costs. CompTIA SecAI+ Study Guide covers AI cost management and resource controls under securing AI systems.
Why D is Correct: Adjusting token limits directly caps the maximum number of tokens used per request for both input and output. By setting appropriate token limits, the organization prevents excessively long prompts or verbose responses from consuming unnecessary tokens, directly translating to lower API costs and providing hard budget control.
Why A is Wrong: Prompt templates standardize how queries are structured, which can indirectly improve efficiency. However, they do not enforce a hard cap on token usage and cannot prevent costs from escalating with large volumes or verbose responses.
Why B is Wrong: Increasing CPU and memory addresses computational infrastructure performance on the client side. LLM API costs are billed by the API provider based on token usage, not on the client ' s hardware resources.
Why C is Wrong: Reducing model size means using a smaller, less powerful model version. While this may lower cost per token, it is a model selection decision, not an ongoing operational control that can be adjusted to manage cost in real time.
NEW QUESTION # 53
An organization wants to reduce vulnerabilities after deployment. The organization decides to incorporate an AI-assisted early detection and vulnerability identification process in its development workflow.
Which of the following AI-assisted functions is the best option?
Answer: D
Explanation:
Basic Concept: Reducing post-deployment vulnerabilities requires catching security issues as early as possible in the development workflow. AI-assisted tools that analyze code during development provide the earliest possible intervention point. CompTIA SecAI+ Study Guide covers AI integration in secure development under AI-assisted security.
Why A is Correct: AI-assisted code linting analyzes source code in real time during development to identify security vulnerabilities, insecure coding patterns, policy violations, and quality issues before code is compiled or committed. By catching vulnerabilities at the coding stage - the earliest possible point in the development workflow - AI code linting prevents vulnerable code from progressing to testing, staging, or production, directly reducing post-deployment vulnerabilities at their source.
Why B is Wrong: Incident management handles security events and incidents after they have occurred in production. It is a reactive capability focused on response and recovery rather than early-stage vulnerability identification in the development workflow.
Why C is Wrong: Automated deployment/rollback automates the process of pushing code to production and reverting to previous versions when issues are detected post-deployment. It is a deployment safety mechanism rather than an early detection tool during the development phase.
Why D is Wrong: System auditing reviews and records system activities and configurations for compliance verification. It is primarily a detective and compliance control for systems that are already deployed, not an early development-phase vulnerability identification tool.
NEW QUESTION # 54
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