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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: 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 - Given a scenario, apply mitigation techniques or controls to secure an environment - Given a scenario, use appropriate tool to assess organizational security - Explain key aspects of digital forensics |
| Topic 2: Attacks, Threats, and Vulnerabilities | 24% | - Given a scenario, analyze potential indicators associated with application attacks - Explain penetration testing concepts - Explain threat actor types and attributes - Compare and contrast types of social engineering attacks - Explain vulnerability scanning concepts - Given a scenario, analyze potential indicators associated with network attacks - Given a scenario, analyze potential indicators to determine the type of attack |
| Topic 3: Architecture and Design | 21% | - Explain secure application development, deployment, and automation concepts - Given a scenario, implement cybersecurity resilience - Explain the importance of security concepts in an enterprise environment - Summarize authentication and authorization design concepts - Summarize basics of cryptographic concepts - Explain the importance of physical security controls - Explain the security implications of embedded and specialized systems - Summarize virtualization and cloud security concepts |
| Topic 4: Implementation | 25% | - Given a scenario, implement secure host settings - Given a scenario, implement secure network architecture concepts - Given a scenario, implement secure systems design - Given a scenario, implement authentication and authorization solutions - Given a scenario, implement public key infrastructure (PKI) - Given a scenario, implement secure mobile device policies - Given a scenario, implement identity and account management controls - Given a scenario, apply cybersecurity solutions to the cloud |
| Topic 5: Governance, Risk, and Compliance | 14% | - Explain risk management processes and concepts - Summarize regulations, standards, and frameworks that impact organizations - 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 |
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NEW QUESTION # 30
Which of the following describes the process of adjusting the distribution of underrepresented classes in an AI model data set to improve model performance?
Answer: C
Explanation:
Data balancing is the process specifically intended to correct disproportionate class distributions within a machine learning dataset. CompTIA SecAI+ lists data balancing as a distinct data-processing concept alongside data cleansing, verification, lineage, provenance, and augmentation. When one class substantially outnumbers another, the model may learn to favor the dominant class and perform poorly on underrepresented examples. Balancing techniques can modify this distribution through approaches such as oversampling minority classes, undersampling majority classes, assigning class weights, or generating suitable additional minority examples. The objective is to give the model adequate exposure to each relevant class and improve generalization across the complete problem space. Data augmentation creates additional or modified training examples and can sometimes contribute to balancing, but augmentation is broader and does not specifically mean adjusting class distribution. Data lineage records where data originated and how it changed throughout its lifecycle. Data cleansing removes or corrects inaccurate, duplicate, malformed, or otherwise unsuitable records. Because the question specifically asks about changing the representation of underrepresented classes, data balancing is the precise CompTIA concept being tested.
NEW QUESTION # 31
Which of the following should an auditor reference when reviewing a company ' s human resources AI systems for legal non-compliance?
Answer: B
Explanation:
Basic Concept: Various regulatory frameworks govern AI use in different contexts. For auditing legal compliance in high-risk AI applications such as employment and HR, binding regulatory legislation takes precedence over voluntary standards. CompTIA SecAI+ Exam Objectives cover AI governance and compliance frameworks under Domain 4.
Why C is Correct: The EU AI Act is the world ' s first comprehensive, legally binding AI regulation. It explicitly classifies AI systems used in employment, worker management, and recruitment as high-risk AI systems, subjecting them to strict compliance requirements including conformity assessments, transparency obligations, and human oversight mandates. An auditor reviewing HR AI for legal non-compliance must reference this binding legislation.
Why A is Wrong: The OECD AI Principles are non-binding international guidelines promoting responsible AI. They offer policy guidance but carry no legal enforcement power for compliance auditing.
Why B is Wrong: The NIST AI RMF is a voluntary, risk management-focused framework. It is not a legal compliance standard and cannot be used to assess legal non-compliance.
Why D is Wrong: ISO standards such as ISO 42001 are voluntary international best practice standards. They are not legal compliance instruments with enforceable penalties for HR AI systems.
NEW QUESTION # 32
A company introduces a large language model (LLM) in an application in order to monitor for a potential denial-of-service attack. Which of the following should the company use to measure the utilization of the LLM?
Answer: B
Explanation:
Tokens are the most appropriate measurement because LLM processing and resource consumption are fundamentally tied to the number of tokens processed in prompts and generated in responses. CompTIA SecAI+ specifically includes token limits among gateway security controls and requires knowledge of AI monitoring for prompts, responses, processing, and cost.
Monitoring token consumption is particularly relevant when investigating denial-of-service or resource- exhaustion conditions. An attacker can submit excessive, repeated, or unusually large inputs that consume the model ' s context capacity and computational resources. Tracking token utilization therefore gives administrators a practical indicator of how heavily the LLM is being consumed and can support thresholds, quotas, rate controls, and anomaly detection.
A transformer is the neural-network architecture underlying many modern LLMs; it is not a unit used to measure utilization. A chain of thoughts refers to intermediate reasoning behavior and does not quantify model consumption. A prompt is an input submitted to the model, but simply counting prompts does not account for their potentially very different sizes. Token measurement therefore provides the most useful utilization metric.
NEW QUESTION # 33
A social media company with more than a million lines of code wants to reduce the mean time to fix bugs and issues.
Which of the following is the most balanced AI strategy to automate the vulnerability management flow?
Answer: B
Explanation:
Basic Concept: Balancing automation with human oversight in vulnerability management requires understanding where AI adds efficiency and where human judgment is irreplaceable. CompTIA SecAI+ Study Guide emphasizes human-in-the-loop principles for high-stakes security decisions, particularly code changes in production systems.
Why A is Correct: Having AI handle triage and ticket creation leverages its ability to rapidly process and categorize large volumes of vulnerability findings, while requiring a software engineer to review and merge code changes maintains essential human oversight for production deployments. This balance maximizes automation benefits (faster triage at scale) while ensuring that actual code modifications to a million-line codebase receive appropriate human review before deployment.
Why B is Wrong: Having humans triage but AI merge code reverses the appropriate division. Manual triage of millions of lines worth of vulnerabilities is where the bottleneck exists. Allowing AI to autonomously merge code changes without human code review oversight creates unacceptable risk of introducing defects or vulnerabilities.
Why C is Wrong: Full manual triage and manual merging eliminates AI automation entirely, failing to address the speed requirement for reducing mean time to fix in a large codebase.
Why D is Wrong: Full AI automation including merging code changes removes essential human oversight from production code deployment. In a million-line codebase, autonomous AI code merging without human review could introduce critical errors or security vulnerabilities.
NEW QUESTION # 34
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 # 35
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