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| Section | Weight | Objectives |
|---|---|---|
| Attacks, Threats, and Vulnerabilities | 24% | - Explain threat actor types and attributes - Given a scenario, analyze potential indicators to determine the type of attack - 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 - Given a scenario, analyze potential indicators associated with application attacks |
| Implementation | 25% | - 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 authentication and authorization solutions - Given a scenario, implement secure mobile device policies - Given a scenario, implement public key infrastructure (PKI) - Given a scenario, implement secure network architecture concepts - Given a scenario, implement identity and account management controls |
| Governance, Risk, and Compliance | 14% | - Explain risk management processes and concepts - Compare and contrast various types of security controls - Given a scenario, follow organizational security policies and procedures - Summarize regulations, standards, and frameworks that impact organizations - Explain privacy and sensitive data concepts in relation to security |
| Operations and Incident Response | 16% | - Given a scenario, use data sources to support an investigation - Explain key aspects of digital forensics - Given a scenario, use appropriate tool to assess organizational security - Summarize the importance of policies, processes, and procedures for incident response - Given a scenario, apply mitigation techniques or controls to secure an environment |
| Architecture and Design | 21% | - Summarize basics of cryptographic concepts - Given a scenario, implement cybersecurity resilience - Summarize authentication and authorization design 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 - Explain the security implications of embedded and specialized systems - Summarize virtualization and cloud security concepts |
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NEW QUESTION # 147
Which of the following is the best example of an AI model that is trained to identify multiple points from input using a neural network to provide output for authentication?
Answer: D
Explanation:
Facial recognition uses neural networks to analyze multiple points or features from an input image (such as eyes, nose, mouth, and facial structure) to generate a unique identifier for authentication purposes.
NEW QUESTION # 148
A security consultant must summarize the impact of posture management on a machine learning (ML) use case.
Which of the following is the most appropriate reference for this purpose?
Answer: A
Explanation:
Basic Concept: Security posture management for AI systems involves assessing and improving the overall security state of AI deployments, including identifying risks, implementing controls, and maintaining ongoing compliance. Appropriate frameworks provide structure for this assessment. CompTIA SecAI+ Study Guide identifies NIST AI RMF as the primary framework for AI risk and posture management.
Why B is Correct: The NIST AI Risk Management Framework provides comprehensive, actionable guidance for managing and improving AI security and risk posture across the entire AI lifecycle. It includes the GOVERN, MAP, MEASURE, and MANAGE functions that directly address posture management activities including risk identification, assessment, and control implementation for ML use cases. Its technical depth and ML-specific guidance make it ideal for this summarization task.
Why A is Wrong: OECD standards provide high-level policy principles for AI governance at an international level. They lack the technical specificity and operational guidance needed to summarize posture management impact on a specific ML use case.
Why C is Wrong: The EU AI Act is a regulatory compliance framework establishing legal requirements for AI systems. While it addresses risk management, its focus is on legal compliance rather than technical posture management guidance for ML systems.
Why D is Wrong: A Generative Adversarial Network is an AI architecture for generating synthetic data, not a framework or standard. It has no relevance as a reference for AI security posture management.
NEW QUESTION # 149
Which log type is MOST useful for detecting DNS tunneling?
Answer: C
Explanation:
DNS logs reveal abnormal query lengths and frequencies typical of tunneling.
NEW QUESTION # 150
An organization recently developed an AI-powered product and discovers that it is vulnerable to attacks in which malicious actors can alter the input, causing the system to recommend inappropriate information.
Which of the following techniques is the most effective way to secure the system against manipulation attacks?
Answer: C
Explanation:
Basic Concept: Input manipulation attacks - including adversarial examples and prompt injection - alter inputs to cause AI systems to produce unintended, harmful, or inappropriate outputs. Defending against these attacks requires mechanisms that validate and constrain both inputs and outputs at runtime. CompTIA SecAI+ Study Guide identifies guardrails as the primary defense against input manipulation in AI systems.
Why D is Correct: Guardrails implement real-time validation and filtering of both incoming inputs and outgoing recommendations. They detect manipulated inputs that deviate from expected patterns, enforce content policies on outputs, and prevent the system from producing inappropriate recommendations regardless of how cleverly the input was crafted. Guardrails provide the most comprehensive and directly applicable defense against the described manipulation attack scenario.
Why A is Wrong: Cross-validation is a model evaluation technique that assesses how well a model generalizes to independent datasets during training. It measures predictive performance but does not provide runtime protection against input manipulation attacks on a deployed system.
Why B is Wrong: Feature regularization is a training technique that adds penalties to model weights to prevent overfitting. It improves generalization during training but does not inspect or validate inputs at inference time to detect manipulation.
Why C is Wrong: Feature scaling normalizes input feature values to a standard range for training efficiency.
Like regularization, it is a preprocessing and training step that has no effect on defending against runtime input manipulation attacks on deployed systems.
NEW QUESTION # 151
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: A
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 # 152
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