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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 appropriate tool to assess organizational security - Explain key aspects of digital forensics - Summarize the importance of policies, processes, and procedures for incident response - Given a scenario, use data sources to support an investigation |
| Topic 2: Attacks, Threats, and Vulnerabilities | 24% | - Compare and contrast types of social engineering attacks - Given a scenario, analyze potential indicators associated with application attacks - Given a scenario, analyze potential indicators to determine the type of attack - Given a scenario, analyze potential indicators associated with network attacks - Explain vulnerability scanning concepts - Explain penetration testing concepts - Explain threat actor types and attributes |
| Topic 3: Governance, Risk, and Compliance | 14% | - Given a scenario, follow organizational security policies and procedures - Compare and contrast various types of security controls - Explain risk management processes and concepts - Explain privacy and sensitive data concepts in relation to security - Summarize regulations, standards, and frameworks that impact organizations |
| Topic 4: Implementation | 25% | - Given a scenario, implement secure host settings - Given a scenario, implement secure network architecture concepts - Given a scenario, implement authentication and authorization solutions - Given a scenario, implement identity and account management controls - Given a scenario, apply cybersecurity solutions to the cloud - Given a scenario, implement public key infrastructure (PKI) - Given a scenario, implement secure mobile device policies - Given a scenario, implement secure systems design |
| Topic 5: Architecture and Design | 21% | - Summarize authentication and authorization design concepts - Explain secure application development, deployment, and automation concepts - Explain the importance of security concepts in an enterprise environment - Summarize virtualization and cloud security concepts - Explain the security implications of embedded and specialized systems - Explain the importance of physical security controls - Given a scenario, implement cybersecurity resilience - Summarize basics of cryptographic concepts |
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NEW QUESTION # 130
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 # 131
Customer feedback for an AI chatbot has a high-rate of non-answers, which is causing higher central processing unit (CPU) utilization. Which of the following should be implemented?
Answer: D
Explanation:
Implementing a response confidence level ensures the chatbot only provides answers when the model is sufficiently confident. This reduces irrelevant or empty responses, improving user experience and lowering unnecessary CPU utilization.
NEW QUESTION # 132
Which of the following controls is the best way to mitigate a denial-of-service (DoS) attack?
Answer: D
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 # 133
A customer-facing, AI-powered chatbot has been jailbroken through prompt injections. As a result, the AI model is offering a 99% discount on the purchase of a new vehicle.
Which of the following should be implemented to enhance the model ' s robustness against such attacks?
Answer: A
Explanation:
Basic Concept: Jailbreaking through prompt injection exploits the LLM ' s tendency to follow instructions embedded in user input, overriding its intended behavior. The model was manipulated to offer unauthorized discounts, demonstrating that its operational boundaries were not properly enforced. CompTIA SecAI+ Study Guide identifies guardrails as the primary defense against jailbreaking attacks.
Why D is Correct: Guardrails are robust, layered controls that enforce behavioral boundaries on LLM inputs and outputs. They can detect and block jailbreaking attempts, enforce business logic constraints such as preventing unauthorized discounts, validate outputs against policy rules before delivery, and prevent the model from operating outside its defined scope. Guardrails are specifically designed to make models more robust against prompt injection and jailbreaking.
Why A is Wrong: Bias filtering is designed to detect and remove biased, discriminatory, or offensive content from model outputs. It addresses content fairness issues but does not prevent jailbreaking attacks that manipulate the model into performing unauthorized actions.
Why B is Wrong: A system prompt sets the model ' s base instructions and persona, but the jailbreak attack already demonstrates that the current prompt can be overridden. Guardrails provide enforcement at a layer that is more resistant to prompt manipulation than the system prompt alone.
Why C is Wrong: Log monitoring detects jailbreaking attempts after they have already succeeded. It is a detective control that enables incident response but does not prevent the model from offering unauthorized discounts in the first place.
NEW QUESTION # 134
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: C
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 # 135
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