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| Section | Weight | Objectives |
|---|---|---|
| AI-assisted Security | 24% | - AI for threat detection and response
|
| Basic AI Concepts Related to Cybersecurity | 17% | - AI-driven threats and risks
|
| AI Governance, Risk and Compliance | 19% | - Governance frameworks and policies
|
| Securing AI Systems | 40% | - Defending against AI-specific attacks
|
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NEW QUESTION # 108
User experience is declining since the launch of a large language model (LLM) in internal networks.
Which of the following should be the highest priority for the prompt engineers?
Answer: C
Explanation:
Basic Concept: Prompt engineers are responsible for designing and refining the prompts and instructions that guide an LLM ' s behavior. When user experience is declining after an LLM launch, this signals that the model ' s outputs are not meeting quality standards. CompTIA SecAI+ addresses prompt engineering quality management under securing and optimizing AI systems.
Why C is Correct: Quality control should be the highest priority when user experience is declining. Prompt engineers must systematically evaluate model responses against quality benchmarks, identify failure patterns causing poor user experience, and iteratively refine prompts to produce accurate, relevant, and appropriately formatted responses. Quality control encompasses testing, evaluation, and continuous improvement of prompt performance.
Why A is Wrong: Customer success management is a business function focused on customer relationship management and retention. While related to user experience outcomes, it is not a technical priority that prompt engineers can directly address through their core competency of prompt design and refinement.
Why B is Wrong: Sales life cycle management is a business process for managing customer acquisition and revenue. It is entirely outside the scope of prompt engineering activities and does not address declining LLM user experience.
Why D is Wrong: Business objectives define what the organization aims to achieve with the LLM deployment. These are set at the strategic level and inform the direction for prompt engineering. They are inputs to the quality control process rather than the priority action prompt engineers should take when experience is declining.
NEW QUESTION # 109
A team of engineers builds an application using a large language model (LLM). The application is built on Linux and is hosted on a virtual server. Users must create an account in order to access and use the platform.
Which of the following should the team do to protect the account credentials?
Answer: D
Explanation:
Basic Concept: User account credentials stored in a database must be protected against unauthorized disclosure. The security of credentials at rest requires cryptographic controls that prevent even database administrators or attackers with database access from reading plaintext passwords. CompTIA SecAI+ Study Guide covers credential security controls as part of AI application security.
Why C is Correct: Implementing hashing and encryption for credential protection is the industry-standard approach. Passwords should be hashed using strong, slow algorithms such as bcrypt, Argon2, or scrypt with unique salts, making them computationally infeasible to reverse even if the database is compromised.
Additional sensitive credential data can be encrypted. Together, hashing and encryption ensure that account credentials remain protected even if the underlying storage is accessed by unauthorized parties.
Why A is Wrong: Patching the model with new datasets updates the AI model ' s training data and knowledge. It does not address the security of user account credentials stored in the application ' s authentication database.
Why B is Wrong: Updating Linux and virtual server software patches system vulnerabilities and is important for overall security hygiene. However, it does not implement specific protections for the account credentials themselves stored in the application database.
Why D is Wrong: Deploying an authenticated API requires users to authenticate to use the API, improving access control. While this complements credential security, it does not protect the storage of credentials at rest and does not replace hashing and encryption of the credential values themselves.
NEW QUESTION # 110
Which of the following would most likely be used to prove that an image is AI generated?
Answer: B
Explanation:
Basic Concept: As AI-generated images become increasingly indistinguishable from authentic photographs, technical mechanisms are needed to definitively identify synthetic content. Embedding verifiable markers in AI-generated content at creation time provides a reliable provenance trail. CompTIA SecAI+ Study Guide covers digital content authentication and AI provenance controls.
Why D is Correct: Watermarking embeds invisible or visible identifying markers into AI-generated images at the point of creation. These watermarks can be cryptographic, steganographic, or perceptual in nature and survive compression, cropping, and other common image manipulations. When an image ' s provenance is questioned, watermark detection tools can analyze it to confirm AI generation. This is the primary technical mechanism for proving AI image origin as supported by the Content Authenticity Initiative and C2PA standards.
Why A is Wrong: Human validation relies on subjective visual inspection by experts to determine if an image appears AI-generated. Modern AI image generation has advanced to the point where human reviewers frequently cannot distinguish synthetic from authentic images, making this approach unreliable for definitive proof.
Why B is Wrong: Guardrails are input and output filtering controls for AI systems. They restrict what content an AI can generate but do not create verifiable proof of an image ' s AI origin after generation.
Why C is Wrong: Diffusion refers to the diffusion model architecture used to generate images. It describes the creation process, not a verification mechanism. Knowing that diffusion models were used does not help prove a specific image was AI-generated after the fact.
NEW QUESTION # 111
A security architect performs threat modeling of an AI system. The architect needs to determine which attacks can be performed against the system.
Which of the following actions should the architect take next?
Answer: A
Explanation:
MITRE ATLAS is specifically designed to catalog adversarial TTPs targeting AI systems. By analyzing ATLAS, the architect can determine which types of attacks are possible against the AI system, making it the most appropriate resource for threat modeling in this context.
NEW QUESTION # 112
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 # 113
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