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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Operations and Incident Response | 16% | - Given a scenario, apply mitigation techniques or controls to secure an environment - Given a scenario, use data sources to support an investigation - Given a scenario, use appropriate tool to assess organizational security - Explain key aspects of digital forensics - Summarize the importance of policies, processes, and procedures for incident response |
| Topic 2: Implementation | 25% | - Given a scenario, implement authentication and authorization solutions - Given a scenario, implement secure systems design - Given a scenario, implement secure network architecture concepts - Given a scenario, implement secure mobile device policies - Given a scenario, apply cybersecurity solutions to the cloud - Given a scenario, implement secure host settings - Given a scenario, implement identity and account management controls - Given a scenario, implement public key infrastructure (PKI) |
| Topic 3: Attacks, Threats, and Vulnerabilities | 24% | - Explain penetration testing concepts - Compare and contrast types of social engineering attacks - Explain vulnerability scanning concepts - Given a scenario, analyze potential indicators associated with application attacks - Given a scenario, analyze potential indicators associated with network attacks - Explain threat actor types and attributes - Given a scenario, analyze potential indicators to determine the type of attack |
| Topic 4: Governance, Risk, and Compliance | 14% | - Given a scenario, follow organizational security policies and procedures - Explain risk management processes and concepts - Explain privacy and sensitive data concepts in relation to security - Compare and contrast various types of security controls - Summarize regulations, standards, and frameworks that impact organizations |
| Topic 5: Architecture and Design | 21% | - Given a scenario, implement cybersecurity resilience - Summarize authentication and authorization design concepts - Summarize basics of cryptographic concepts - Explain the importance of security concepts in an enterprise environment - Explain the security implications of embedded and specialized systems - Explain secure application development, deployment, and automation concepts - Summarize virtualization and cloud security concepts - Explain the importance of physical security controls |
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NEW QUESTION # 86
Which of the following should an auditor reference when reviewing a company's human resources AI systems for legal non-compliance?
Answer: C
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 # 87
Which of the following controls is the best way to mitigate a denial-of-service (DoS) attack?
Answer: A
Explanation:
Rate limiting restricts the number of requests within a set timeframe, preventing attackers from overwhelming the system with excessive traffic, making it the best control to mitigate a DoS attack.
NEW QUESTION # 88
Developers introduce new features to their generative AI product in an effort to stand out from the competition and offer more value to customers.
Which of the following most accurately explains the risks when enabling more functionality?
Answer: B
Explanation:
Basic Concept: The relationship between AI system capabilities and security risk is a fundamental concept in AI governance. As AI models gain more functionality and capabilities, their potential for misuse, unintended consequences, and attack surface expansion grows proportionally. CompTIA SecAI+ Study Guide addresses capability-risk proportionality under AI governance.
Why D is Correct: The risks of a generative AI product are proportional to its capabilities. Each new feature expands what the model can do, which simultaneously expands what adversaries can manipulate it to do, what sensitive operations it can be directed to perform, and what unintended harm it can cause. A model that can generate text, images, execute code, and call external APIs has dramatically greater risk potential than one that can only generate text. Risk grows with capability scope.
Why A is Wrong: Risks do not remain constant when new features are added. New features introduce new attack vectors, expand the model ' s action space, and create new opportunities for misuse. Each addition fundamentally changes the system ' s risk profile.
Why B is Wrong: While risks do increase with new features, saying they simply increase does not capture the precise relationship. The increase is proportional to the nature and scope of the capabilities added, not a uniform increment for any feature addition.
Why C is Wrong: While risks can be measured qualitatively, stating that risks are measured qualitatively is a statement about measurement methodology rather than an explanation of how risks change when functionality is enabled. It does not accurately describe the relationship between capability and risk.
NEW QUESTION # 89
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: B
Explanation:
Basic Concept: Neural networks can be trained to identify and match complex multi-point patterns in data.
For biometric authentication, facial recognition uses deep neural networks to extract and compare dozens to hundreds of facial feature points from an input image. CompTIA SecAI+ covers neural network-based authentication under basic AI concepts.
Why A is Correct: Facial recognition systems use neural networks specifically trained to identify multiple facial landmarks and feature points such as eye distance, nose shape, and jawline contour from input images.
These extracted feature vectors are compared against stored templates to authenticate individuals, making this the ideal example of multi-point neural network-based authentication output.
Why B is Wrong: An encryption key is a cryptographic artifact, not an AI model output. Encryption keys are generated mathematically, not through neural network training or multi-point feature identification from biometric inputs.
Why C is Wrong: OAuth is an open authorization protocol framework that handles delegated access and permission grants between services. It is an authentication delegation standard, not an AI model that processes input through neural networks.
Why D is Wrong: A bounding box is an output of object detection models that draws a rectangular box around detected objects in an image. While it uses neural networks, it identifies object location rather than multiple feature points for authentication purposes.
NEW QUESTION # 90
An AI security administrator receives an inquiry about an unusually high monthly bill from the AI solution provider. The administrator thinks the majority of staff might be using the most powerful model available.
Which of the following AI measures should the administrator implement to lower costs?
Answer: A
Explanation:
Basic Concept: LLM API billing is primarily based on token consumption. High costs resulting from staff using powerful, verbose models can be controlled by limiting the maximum tokens processed per interaction and restricting which models staff can access. CompTIA SecAI+ Study Guide covers token management as the primary cost control mechanism for AI deployments.
Why D is Correct: Implementing token limits caps the maximum tokens consumed per API call for both input and output. This directly controls the per-interaction cost by preventing excessively long prompts or overly verbose model responses from generating large token bills. Combined with model tier restrictions, token limits ensure that interactions with powerful models remain within budget constraints regardless of how extensively staff use the service.
Why A is Wrong: Storage monitoring tracks the utilization and performance of data storage systems. Storage costs are separate from LLM API token-based billing and monitoring storage does not address the high API charges resulting from excessive model usage by staff.
Why B is Wrong: Modality types refer to the input formats an AI model accepts such as text, images, audio, or video. While different modalities have different pricing, managing modality types is not the direct cost control lever. Token consumption is the primary cost driver for text-based interactions.
Why C is Wrong: Prompt firewalls inspect and filter prompt content for security and policy compliance.
While they can block certain types of queries, they are designed for security purposes, not as financial controls to limit token consumption or enforce cost budgets across staff usage.
NEW QUESTION # 91
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