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| Section | Weight | Objectives |
|---|---|---|
| Architecture and Design | 21% | - Explain the importance of physical security controls - Given a scenario, implement cybersecurity resilience - Summarize virtualization and cloud security concepts - Summarize authentication and authorization design concepts - Explain secure application development, deployment, and automation concepts - Explain the importance of security concepts in an enterprise environment - Explain the security implications of embedded and specialized systems - Summarize basics of cryptographic concepts |
| Operations and Incident Response | 16% | - Given a scenario, use data sources to support an investigation - Summarize the importance of policies, processes, and procedures for incident response - Explain key aspects of digital forensics - Given a scenario, use appropriate tool to assess organizational security - Given a scenario, apply mitigation techniques or controls to secure an environment |
| Implementation | 25% | - Given a scenario, implement authentication and authorization solutions - Given a scenario, apply cybersecurity solutions to the cloud - 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, implement identity and account management controls |
| Governance, Risk, and Compliance | 14% | - 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 - Explain privacy and sensitive data concepts in relation to security |
| Attacks, Threats, and Vulnerabilities | 24% | - Explain vulnerability scanning concepts - Given a scenario, analyze potential indicators associated with application attacks - Compare and contrast types of social engineering attacks - Explain threat actor types and attributes - Given a scenario, analyze potential indicators to determine the type of attack - Explain penetration testing concepts - Given a scenario, analyze potential indicators associated with network attacks |
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NEW QUESTION # 137
Which of the following is the primary purpose of validating data for an AI system?
Answer: B
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 # 138
A group of security engineers is developing a security incident and event management (SIEM) system that will:
- Be able to ingest data from multiple structured and unstructured
sources.
- Have a chatbot integrated with a large language model (LLM) that the
security analyst can interact with.
- Provide insights from the SIEM alert data.
Which of the following techniques should the security engineers consider before collecting the data from the respective sources?
Answer: C
Explanation:
Data cleansing ensures that structured and unstructured data ingested into the SIEM is accurate, consistent, and free from errors or irrelevant information. This step is critical before integrating with an LLM chatbot, as clean data improves the reliability and quality of insights generated.
NEW QUESTION # 139
A team of data scientists is ready to release a model for enterprise use. The team wants to protect the model from unintentional changes or tampering.
Which of the following is the most appropriate action?
Answer: B
Explanation:
Basic Concept: Protecting a released AI model from unauthorized modification requires controlling who can interact with it and at what privilege level. IAM-integrated API access provides granular, auditable control over model interactions. CompTIA SecAI+ Study Guide covers model protection through identity and access management integration.
Why D is Correct: Integrating an API with IAM roles ensures that all interactions with the model are authenticated and authorized according to precisely defined permissions. IAM roles enforce the principle of least privilege, ensuring users can query the model only within authorized scope and cannot modify model parameters, weights, or configuration. API-level access provides an abstraction layer that protects the underlying model from direct access while enabling controlled, auditable interactions.
Why A is Wrong: Changing to an LLM with guardrails addresses model behavior safety but does not protect the model artifacts themselves from tampering or unauthorized modification. It changes the model type rather than implementing access controls.
Why B is Wrong: Providing secure copies for local runtime distributes model copies to multiple endpoints, significantly increasing the attack surface for tampering. Each local copy represents a potential point of unauthorized modification.
Why C is Wrong: Restricting access to IT professionals is overly broad and vague. IT professionals may still need varying levels of access for different purposes, and generic role-based access without IAM integration and API mediation provides insufficient granularity to prevent unintentional modification.
NEW QUESTION # 140
A data set containing medical information is put into a machine learning (ML) model that is designed to predict specific illnesses for a population. In the process of verifying the reliability of the system, the compliance officer realizes that the system cannot reliably predict illnesses for certain segments of the population.
Which of the following types of risk is most applicable to this case?
Answer: D
Explanation:
Basic Concept: AI models trained on unrepresentative data can produce systematically inaccurate results for certain population groups. This is a form of algorithmic bias where the model ' s performance varies significantly across demographic segments, creating disparate outcomes. CompTIA SecAI+ Exam Objectives cover bias as a core AI governance and risk concept.
Why A is Correct: Bias in AI occurs when a model produces systematically skewed results for certain groups due to biased training data, flawed data collection, or model design choices. In this healthcare scenario, the inability to reliably predict illnesses for specific population segments indicates the training data likely underrepresented those segments, causing the model to learn inadequate patterns for them. This is a critical bias risk with serious health equity implications.
Why B is Wrong: Consistency refers to the model producing the same output given the same input across different runs or time periods. The problem described is not about inconsistent outputs for the same input but about systematically poor performance for specific population groups.
Why C is Wrong: Transparency refers to openness about how the AI model operates, what data it uses, and how it makes decisions. The compliance officer has already assessed the system, suggesting sufficient transparency exists to identify the performance gap.
Why D is Wrong: Inclusiveness is a design principle ensuring AI systems are designed to serve all users regardless of background. While related to the outcome, the specific risk type described - differential predictive accuracy across population segments - is most precisely categorized as bias.
NEW QUESTION # 141
A security operations center (SOC) analyst needs to automate multiple security tasks by breaking them down into smaller parts. Which of the following AI tools is the best for this task?
Answer: D
Explanation:
Agentic AI is designed to autonomously break down complex tasks into smaller steps and execute them in sequence. This makes it the best tool for automating multiple security tasks in a SOC environment.
NEW QUESTION # 142
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