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| Section | Weight | Objectives |
|---|---|---|
| Securing AI Systems | 40% | - AI System Protection
|
| AI Governance, Risk, and Compliance | 19% | - Risk and Compliance
|
| AI-Assisted Security | 24% | - Security Operations Enhancement
|
| Basic AI Concepts Related to Cybersecurity | 17% | - AI and Machine Learning Fundamentals
|
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NEW QUESTION # 107
Which of the following is the most impactful security risk associated with the use of a generative AI chatbot?
Answer: A
Explanation:
Basic Concept: Generative AI chatbots interact with users in natural language and may access organizational knowledge bases, databases, or prior conversations. The conversational nature of these systems creates unique risks around sensitive information disclosure. CompTIA SecAI+ Study Guide ranks data leakage as the primary security concern for generative AI chatbots.
Why B is Correct: Data leakage occurs when a generative AI chatbot inadvertently reveals sensitive information including PII, confidential business data, intellectual property, training data, or system configurations in its responses. This can happen through prompt injection attacks, insufficient output filtering, or the model memorizing and reproducing sensitive training data. The impact is immediate, potentially irreversible, and can result in regulatory violations, competitive disadvantage, and reputational damage.
Why A is Wrong: Overly permissive access is a contributing factor that can exacerbate data leakage but is an access control design issue rather than the most directly impactful runtime risk of operating a generative AI chatbot.
Why C is Wrong: Weak encryption is a data protection concern for data in transit or at rest. While important, it is a configuration issue separate from the generative AI chatbot ' s core operational risks and is not specific to chatbot technology.
Why D is Wrong: Model validation ensures a model performs as expected before deployment. While important for quality assurance, it is a development lifecycle activity rather than an ongoing operational security risk associated with running a chatbot.
NEW QUESTION # 108
A cybersecurity analyst must use pattern recognition on a data set containing unstructured data.
Which of the following models is the best for this task?
Answer: B
Explanation:
Basic Concept: Different ML model architectures are optimized for different data types and tasks.
Unstructured data such as images, raw network packet captures, and visual content requires models capable of automatically extracting hierarchical spatial features. CompTIA SecAI+ covers ML model selection for security tasks under basic AI concepts.
Why B is Correct: Convolutional Neural Networks (CNNs) are specifically designed for pattern recognition in unstructured data, particularly image and grid-structured data. CNNs use convolutional layers to automatically extract local and hierarchical features without requiring manual feature engineering. They excel at recognizing patterns in raw, unstructured inputs, making them the optimal choice for pattern recognition on unstructured datasets in cybersecurity contexts such as image-based malware analysis or visual traffic pattern recognition.
Why A is Wrong: Long Short-Term Memory (LSTM) networks are recurrent neural networks optimized for sequential and time-series data such as network traffic flows over time or log sequences. While they handle unstructured sequential data, they are not specifically designed for spatial pattern recognition in general unstructured data.
Why C is Wrong: Decision trees work on structured, tabular data with defined features. They require feature extraction and engineering before processing unstructured data and are not designed for raw pattern recognition in unstructured inputs.
Why D is Wrong: Logistic regression is a linear classification algorithm that requires structured, numerical input features. It cannot directly process unstructured data and requires extensive preprocessing and feature extraction, making it unsuitable for pattern recognition on raw unstructured datasets.
NEW QUESTION # 109
Which of the following certifies an organization in the use of responsible AI?
Answer: A
Explanation:
ISO/IEC 42001 is the correct answer because it is an Artificial Intelligence Management System (AIMS) standard that organizations can implement to demonstrate structured and responsible governance of AI.
CompTIA SecAI+ explicitly includes ISO AI standards within the governance, risk, and compliance domain and expects candidates to distinguish standards from regulatory frameworks. ISO/IEC 42001 specifies requirements for establishing, implementing, maintaining, and continually improving an AI management system. Its scope includes responsible AI use, governance, risk management, accountability, transparency, and organizational controls. ISO describes the standard as applicable to organizations that develop, provide, or use AI systems and specifically identifies responsible AI and AI governance among its benefits. GDPR is legally binding privacy legislation rather than an AI management-system certification. The EU AI Act is also legislation and establishes regulatory requirements based on AI risk. NIST AI RMF provides voluntary risk- management guidance but is not itself an organizational certification standard. Therefore, ISO/IEC 42001 is the option specifically designed to support certifiable organizational AI-management practices.
NEW QUESTION # 110
Which of the following corporate policies would a security architect most likely reference when configuring an AI system to conduct input filtering?
Answer: D
Explanation:
Data governance is the most relevant corporate policy because input filtering depends on knowing what information may enter the AI system, how that information is classified, and which categories of sensitive or regulated data must be blocked, redacted, masked, or otherwise controlled. CompTIA SecAI+ specifically includes sensitive data governance under corporate AI policies and separately identifies data classification labels, redaction, masking, minimization, and related data-safety controls. A security architect can therefore translate the organization ' s data governance rules into technical input-filtering requirements, such as preventing credentials, customer PII, financial information, confidential intellectual property, or restricted datasets from being submitted to a model. A public-model usage policy governs whether and how employees may use externally hosted models, but it is narrower than the underlying data-handling rules. Third-party compliance evaluates external providers and contractual or regulatory obligations. Terms of use describe permitted user behavior but normally do not provide the detailed data-classification requirements required to implement technical filtering. Data governance is therefore the authoritative basis for determining what input the AI system should accept or reject.
NEW QUESTION # 111
A human resources officer is using AI to evaluate resumes and help select candidates that meet minimum criteria. To improve the results, the human resources officer adjusts the query parameters and includes an example resume that matches a successful candidate.
Which of the following best describes this query?
Answer: C
Explanation:
Basic Concept: Prompting techniques determine how effectively an LLM is guided to produce desired outputs. Providing a single example within a prompt is a well-established technique known as one-shot prompting, which leverages in-context learning. CompTIA SecAI+ Study Guide covers prompting strategies under basic AI concepts.
Why C is Correct: One-shot prompting involves providing exactly one example of the desired input-output format within the prompt to guide the model ' s responses. In this scenario, the HR officer includes one example resume matching a successful candidate to show the model what a qualifying candidate looks like.
This single example instructs the model on the evaluation criteria through demonstration rather than explicit description.
Why A is Wrong: Distillation is a model training technique where a smaller student model is trained to replicate the behavior of a larger teacher model. It is a model compression methodology, not a prompting technique used at query time.
Why B is Wrong: A prompt template is a reusable, structured format for prompts with placeholders that can be filled in for different queries. While templates may incorporate examples, the term specifically describes the structural framework, not the act of providing a single example.
Why D is Wrong: A system role defines the AI model ' s persona, context, and behavioral guidelines at the system level before user interaction begins. It sets the model ' s overall behavior, not a specific technique of providing examples within queries.
NEW QUESTION # 112
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