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| Section | Weight | Objectives |
|---|---|---|
| Governance, Risk, and Compliance | 14% | - Explain privacy and sensitive data concepts in relation to security - Compare and contrast various types of security controls - Given a scenario, follow organizational security policies and procedures - Explain risk management processes and concepts - Summarize regulations, standards, and frameworks that impact organizations |
| Architecture and Design | 21% | - Given a scenario, implement cybersecurity resilience - Summarize virtualization and cloud security concepts - Explain the importance of physical security controls - Explain the importance of security concepts in an enterprise environment - Summarize authentication and authorization design concepts - Explain the security implications of embedded and specialized systems - Summarize basics of cryptographic concepts - Explain secure application development, deployment, and automation concepts |
| Operations and Incident Response | 16% | - Given a scenario, use appropriate tool to assess organizational security - 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 |
| Attacks, Threats, and Vulnerabilities | 24% | - 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 - Explain vulnerability scanning concepts - Compare and contrast types of social engineering attacks - Given a scenario, analyze potential indicators associated with application attacks |
| Implementation | 25% | - Given a scenario, implement authentication and authorization solutions - Given a scenario, implement secure mobile device policies - Given a scenario, implement secure network architecture concepts - 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, apply cybersecurity solutions to the cloud - Given a scenario, implement identity and account management controls |
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NEW QUESTION # 14
An AI security administrator notices that the information referenced by the model is incorrectly formatted and missing values.
Which of the following job roles would most likely be responsible for correcting this error?
Answer: D
Explanation:
Basic Concept: In AI teams, different roles hold distinct responsibilities for specific aspects of the AI system.
Data quality issues such as incorrect formatting and missing values fall within the domain of data engineering, which is responsible for designing, building, and maintaining the data pipelines and datasets that feed AI models. CompTIA SecAI+ Study Guide covers AI team role definitions under governance and basic AI concepts.
Why C is Correct: A Data Engineer is responsible for building and maintaining the data pipelines, transformation processes, and data quality controls that supply AI models with properly formatted, complete data. Addressing incorrectly formatted data and missing values is a core data engineering responsibility, as these professionals own the ETL (Extract, Transform, Load) processes, data validation rules, and data quality frameworks that ensure the model receives clean, usable input.
Why A is Wrong: A Platform Engineer designs and maintains the computing infrastructure and platforms that host AI systems. Their focus is the technical environment and deployment infrastructure, not the quality or formatting of the data consumed by models.
Why B is Wrong: An MLOps Engineer manages the deployment, monitoring, and operational lifecycle of AI models in production. While they may detect data quality issues through monitoring, resolving data formatting and missing value problems is the responsibility of data engineers who own the data pipelines.
Why D is Wrong: An AI Architect designs the overall AI system architecture, component interactions, and technical strategy. While they define data requirements, the hands-on work of correcting data formatting errors and filling missing values belongs to the data engineering function.
NEW QUESTION # 15
Which of the following should an auditor reference when reviewing a company's human resources AI systems for legal non-compliance?
Answer: A
Explanation:
The EU AI Act is legally binding legislation that specifically governs the use of AI systems, including those used in human resources for hiring, promotion, and evaluation. An auditor reviewing AI systems for legal non-compliance must reference this act because it establishes enforceable requirements related to transparency, bias, risk classification, and prohibited practices.
NEW QUESTION # 16
During an update, an AI system flags some potential compatibility issues and provides recommendations. An administrator reviews the recommendations before addressing the issues.
Which of the following processes describes this scenario?
Answer: D
Explanation:
Basic Concept: Human-in-the-loop is a design pattern where AI systems generate recommendations or decisions but require human review and approval before those recommendations are acted upon. This approach maintains human oversight and accountability in AI-assisted workflows. CompTIA SecAI+ Study Guide covers human-in-the-loop as a key responsible AI principle and operational pattern.
Why C is Correct: The scenario precisely describes the human-in-the-loop pattern: the AI system identifies potential issues and provides recommendations, but an administrator must review those recommendations before any action is taken. This deliberate inclusion of human judgment in the AI ' s decision or recommendation workflow ensures human oversight is maintained, which is the defining characteristic of the human-in-the-loop process.
Why A is Wrong: Data validation verifies that data meets expected quality standards and formats before being used in AI processing. It is a data quality control activity, not a workflow pattern describing human review of AI recommendations.
Why B is Wrong: Data preparation involves transforming raw data into a format suitable for AI model training or inference. It encompasses cleaning, normalizing, and formatting data, not the process of human review of AI-generated recommendations during system updates.
Why D is Wrong: Model evaluation assesses a model ' s performance against metrics such as accuracy, precision, and recall on test datasets. It is a technical assessment of model quality, not a workflow process where humans review AI-generated recommendations before acting on them.
NEW QUESTION # 17
Which of the following improves the observability and auditing of an AI system?
Answer: B
Explanation:
Basic Concept: Observability in AI systems refers to the ability to monitor, log, trace, and audit the behavior of AI models in production. MLOps is the operational discipline that establishes the processes, tooling, and practices for managing AI systems throughout their lifecycle. CompTIA SecAI+ Study Guide covers MLOps as a key mechanism for AI system transparency and auditability.
Why C is Correct: MLOps implements comprehensive monitoring, logging, versioning, and audit pipelines for AI systems. It provides observability through model performance tracking, data drift detection, prediction logging, lineage tracking, and audit trails. MLOps platforms enable organizations to understand what their AI models are doing, why they are making certain decisions, and how their behavior changes over time, directly improving observability and auditing.
Why A is Wrong: Redeploying a model is an operational action taken to restore a previous version or apply updates. It does not improve monitoring infrastructure, logging capabilities, or auditing frameworks for ongoing observability.
Why B is Wrong: Manual detection relies on human observation to identify issues. It is labor-intensive, inconsistent, and not scalable for AI systems processing high volumes of data. It does not provide systematic observability or comprehensive audit trails.
Why D is Wrong: Anomaly detection identifies unusual patterns in data or behavior. While useful as a monitoring component within an observability strategy, it is a single technique and does not encompass the full observability and auditing capabilities provided by a comprehensive MLOps implementation.
NEW QUESTION # 18
A machine learning (ML) engineer is working with a security engineer to identify the best practices for securing a system with various AI models.
Which of the following actions should the engineers suggest?
Answer: A
Explanation:
Basic Concept: Securing AI systems requires a structured, end-to-end approach that addresses security at every phase of the AI model ' s lifecycle from data collection through training, testing, deployment, and ongoing monitoring. CompTIA SecAI+ Study Guide identifies the Model Development Life Cycle as the foundational framework for AI system security.
Why B is Correct: A secure Model Development Life Cycle (MDLC) integrates security practices at every stage of AI model development specifically tailored to ML workflows. It encompasses secure data handling, training data validation, model testing for adversarial robustness, secure deployment practices, and ongoing monitoring. Unlike generic software development lifecycles, the MDLC addresses ML-specific risks such as data poisoning, model drift, and adversarial attacks.
Why A is Wrong: Guardrail testing and security validation are important components of the MDLC but represent only the testing phase. They do not encompass the full lifecycle of security practices needed from data acquisition through production monitoring.
Why C is Wrong: Implementing comprehensive security architecture is a broad statement that describes an outcome rather than a specific actionable practice. It does not provide the structured, ML-specific guidance of an MDLC.
Why D is Wrong: A secure SDLC is designed for traditional software development and covers code security, testing, and deployment. While relevant to AI application development, it does not specifically address ML model-specific risks such as training data security, model integrity, and inference-time attacks.
NEW QUESTION # 19
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