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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: AI-Assisted Security | 24% | - Operational Use of AI
|
| Topic 2: Securing AI Systems | 40% | - Adversarial Defense
|
| Topic 3: Basic AI Concepts Related to Cybersecurity | 17% | - AI Threat Landscape
|
| Topic 4: AI Governance, Risk, and Compliance | 19% | - AI Governance Frameworks
|
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NEW QUESTION # 119
A cyberthreat intelligence (CTI) analyst conducts research about a threat during a recent attack. The analyst uses an AI threat-modeling resource to find the relevant risks and utilizes causal and domain taxonomies.
Which of the following best describes which resource the analyst is using?
Answer: C
Explanation:
The MIT AI Risk Repository is specifically characterized by its use of both a Causal Taxonomy and a Domain Taxonomy. The repository consolidates AI risks from numerous frameworks and allows practitioners to classify and research risks according to how, when, and why they occur as well as the domain of harm involved. MIT states that its Causal Taxonomy addresses factors such as the responsible entity, intent, and timing, while its Domain Taxonomy categorizes risks into broader domains and subdomains. This terminology directly matches the scenario. CompTIA SecAI+ includes the MIT AI Risk Repository among the threat-modeling resources candidates must understand. MITRE ATLAS instead organizes adversarial AI activity into tactics, techniques, procedures, and case studies. The CVE AI Working Group focuses on vulnerabilities and related vulnerability-management concerns rather than causal/domain risk taxonomies.
OWASP ' s Machine Learning Security Top 10 concentrates on prominent ML security risks and defensive awareness. Because the question explicitly identifies causal and domain taxonomies, the MIT AI Risk Repository is the precise resource being described.
NEW QUESTION # 120
Which of the following is the most concerning risk for a company that allows corporate end users to use public-facing large language models (LLMs)?
Answer: C
Explanation:
Basic Concept: When employees interact with public-facing LLMs, any data they input may be processed, logged, or used for model training by the third-party provider. This creates serious regulatory and compliance risks, particularly when sensitive corporate or customer data is involved. CompTIA SecAI+ flags data governance and regulatory compliance as primary concerns in enterprise AI adoption.
Why C is Correct: Submitting sensitive business data, PII, financial records, or proprietary information to public LLMs may violate data protection regulations such as GDPR, HIPAA, or CCPA. These violations can result in substantial fines, legal liability, and significant reputational damage. This represents the most severe and impactful organizational risk from corporate use of public LLMs.
Why A is Wrong: Hallucinations where LLMs generate plausible but incorrect information are a reliability concern. However, they do not expose the company to the level of legal and financial penalties that data regulatory violations create.
Why B is Wrong: Out-of-date acceptable use policies represent an internal governance gap. This is a policy management issue rather than a direct risk with immediate legal or financial consequences.
Why D is Wrong: While LLMs can potentially generate malicious code if deliberately prompted, this requires adversarial intent. For typical corporate users, accidental data disclosure to a public LLM represents a far more common and immediate organizational risk.
NEW QUESTION # 121
Which of the following is the first step before using an online data set?
Answer: D
Explanation:
A third-party risk management assessment is the best first step because an externally obtained online dataset introduces risks concerning source trustworthiness, provenance, licensing, privacy, integrity, security, and regulatory compliance. CompTIA SecAI+ requires candidates to understand third-party compliance evaluations as part of AI governance and also emphasizes trustworthiness and authenticity during the AI lifecycle ' s data-collection stage. Before incorporating an externally sourced dataset into model development or training, the organization should determine who created or controls the data, whether its collection and distribution are legitimate, whether contractual or usage restrictions exist, and whether sensitive or malicious content could be present. A legal review may subsequently be required for licensing, copyright, privacy, and contractual issues, but third-party risk assessment provides the broader initial evaluation. A feasibility study addresses whether the proposed project is practical from a business perspective rather than whether the dataset is trustworthy. Model design review occurs later and focuses on architecture and implementation.
Consequently, third-party risk management provides the most comprehensive initial gate before introducing an online dataset into the organization ' s AI lifecycle.
NEW QUESTION # 122
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 # 123
During a model validation procedure, an engineer notices that a model performs well during training but poorly during testing.
Which of the following best describes the reason?
Answer: A
Explanation:
Basic Concept: The gap between training performance and test performance is a classic indicator of a specific model quality problem. Understanding this phenomenon and its causes is fundamental to AI model development. CompTIA SecAI+ Study Guide covers overfitting under basic AI concepts and model quality.
Why B is Correct: Overfitting occurs when a model learns the training data too specifically - memorizing noise, outliers, and specific patterns in the training set rather than learning generalizable underlying patterns.
The model achieves high accuracy on training data but fails to generalize to new, unseen test data. This produces exactly the scenario described: excellent training performance combined with poor test performance.
Overfitting is the quintessential cause of this training-testing performance gap.
Why A is Wrong: Fine-tuning is a training technique that adapts a pre-trained model to a new task or domain using additional training data. It is a deliberate training process, not a description of why a model ' s performance degrades from training to testing.
Why C is Wrong: Regularization is a training technique specifically used to prevent overfitting by adding penalties to large model weights, encouraging the model to learn simpler, more generalizable patterns. It is the solution to overfitting, not its cause.
Why D is Wrong: Inference is the process of using a trained model to make predictions on new data. It describes the operational use of a model, not a quality characteristic that explains why performance differs between training and testing phases.
NEW QUESTION # 124
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