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| Section | Weight | Objectives |
|---|---|---|
| Architecture and Design | 21% | - Summarize authentication and authorization design concepts - Summarize virtualization and cloud security concepts - Explain the security implications of embedded and specialized systems - Given a scenario, implement cybersecurity resilience - Summarize basics of cryptographic concepts - Explain the importance of security concepts in an enterprise environment - Explain the importance of physical security controls - Explain secure application development, deployment, and automation concepts |
| Implementation | 25% | - Given a scenario, implement secure network architecture concepts - Given a scenario, implement secure mobile device policies - Given a scenario, implement public key infrastructure (PKI) - Given a scenario, implement secure host settings - Given a scenario, implement authentication and authorization solutions - Given a scenario, implement secure systems design - Given a scenario, apply cybersecurity solutions to the cloud - Given a scenario, implement identity and account management controls |
| Governance, Risk, and Compliance | 14% | - Given a scenario, follow organizational security policies and procedures - Compare and contrast various types of security controls - Summarize regulations, standards, and frameworks that impact organizations - Explain privacy and sensitive data concepts in relation to security - Explain risk management processes and concepts |
| Attacks, Threats, and Vulnerabilities | 24% | - Given a scenario, analyze potential indicators associated with application attacks - 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 to determine the type of attack - Compare and contrast types of social engineering attacks - Explain vulnerability scanning concepts |
| Operations and Incident Response | 16% | - Summarize the importance of policies, processes, and procedures for incident response - Explain key aspects of digital forensics - Given a scenario, use data sources to support an investigation - Given a scenario, apply mitigation techniques or controls to secure an environment - Given a scenario, use appropriate tool to assess organizational security |
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NEW QUESTION # 96
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: D
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 # 97
Which of the following is the primary purpose of validating data for an AI system?
Answer: D
Explanation:
Basic Concept: Data validation is a critical step in the AI development pipeline that involves verifying that the data used for training and inference meets quality standards, is representative, and is free from systematic errors that could introduce bias. The quality of training data directly determines the quality and fairness of model outputs. CompTIA SecAI+ Study Guide covers data validation under AI development and responsible AI principles.
Why D is Correct: The primary purpose of data validation for an AI system is to ensure that the data is accurate, representative, and free from systematic errors that would cause the model to produce biased or discriminatory outcomes. Validating data checks for class imbalance, demographic underrepresentation, labeling errors, and corrupted values that could embed biases into the model. This ensures the AI system produces fair, accurate, and trustworthy outputs across all user groups.
Why A is Wrong: Automating the process is a benefit of using automated data validation tools but is not the primary purpose of validation itself. The automation serves the validation goal rather than being the reason validation is performed.
Why B is Wrong: Reducing resource consumption is an engineering optimization concern. Data validation may reduce resource waste by preventing training on poor-quality data, but this is a secondary benefit, not the primary purpose.
Why C is Wrong: Optimizing storage databases is a database engineering concern about performance and efficiency. Data validation examines data quality and representativeness for AI purposes, not database architecture optimization.
NEW QUESTION # 98
Which of the following describe the practice of providing examples in a prompt? (Choose two.)
Answer: A,F
Explanation:
Providing examples in a prompt is referred to as one-shot prompting when a single example is given and multi-shot prompting when multiple examples are provided.
NEW QUESTION # 99
Which of the following roles best supports the implementation of AI governance, risk, and compliance (GRC)? (Choose two.)
Answer: D,F
Explanation:
Basic Concept: AI GRC implementation requires roles that combine understanding of AI technical capabilities and limitations with security risk assessment, control design, and compliance framework expertise. Identifying which roles naturally contribute to AI GRC is essential for team design. CompTIA SecAI+ Study Guide covers AI governance role responsibilities under Domain 4.
Why B is Correct: Data Scientists possess deep understanding of AI model capabilities, limitations, data requirements, and failure modes. For GRC implementation, their technical expertise is essential for identifying AI-specific risks such as bias, model drift, and data quality issues, assessing compliance implications of model design choices, and evaluating whether AI systems meet governance requirements.
Why D is Correct: Security Architects design comprehensive security frameworks and risk management strategies. For AI GRC, they translate governance requirements into technical controls, design AI security architectures that satisfy compliance obligations, assess the risk posture of AI deployments, and ensure security principles including least privilege, defense-in-depth, and audit logging are built into AI system designs.
Why A is Wrong: Desktop specialists manage user workstation hardware and software. Their role focuses on endpoint management and user support, not on the strategic risk assessment, compliance evaluation, or technical AI governance activities required for AI GRC implementation.
Why C is Wrong: Software developers write application code. While they implement security controls when directed, they typically lack the broad risk management, compliance framework expertise, and security architecture perspective needed to lead AI GRC implementation.
Why E is Wrong: SOC analysts focus on monitoring, detecting, and responding to security incidents in operational environments. Their expertise is in reactive security operations rather than the proactive governance framework design and compliance management that AI GRC requires.
Why F is Wrong: Network engineers design and maintain network infrastructure. Their expertise is in network connectivity and protocols, not in AI system governance, risk assessment frameworks, or compliance requirements.
NEW QUESTION # 100
What control reduces the impact radius when a single host is compromised?
Answer: B
Explanation:
Segmentation isolates systems and limits lateral movement.
NEW QUESTION # 101
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