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Each question shows the correct answer and an explanation of why it is right
Which of the following improves the observability and auditing of an AI system?
Basic Concept: Observability in AI systems refers to the ability to monitor, log, trace, and audit the behavior of AI models in production. MLOps is the operational discipline that establishes the processes, tooling, and practices for managing AI systems throughout their lifecycle. CompTIA SecAI+ Study Guide covers MLOps as a key mechanism for AI system transparency and auditability.
Why C is Correct: MLOps implements comprehensive monitoring, logging, versioning, and audit pipelines for AI systems. It provides observability through model performance tracking, data drift detection, prediction logging, lineage tracking, and audit trails. MLOps platforms enable organizations to understand what their AI models are doing, why they are making certain decisions, and how their behavior changes over time, directly improving observability and auditing.
Why A is Wrong: Redeploying a model is an operational action taken to restore a previous version or apply updates. It does not improve monitoring infrastructure, logging capabilities, or auditing frameworks for ongoing observability.
Why B is Wrong: Manual detection relies on human observation to identify issues. It is labor-intensive, inconsistent, and not scalable for AI systems processing high volumes of data. It does not provide systematic observability or comprehensive audit trails.
Why D is Wrong: Anomaly detection identifies unusual patterns in data or behavior. While useful as a monitoring component within an observability strategy, it is a single technique and does not encompass the full observability and auditing capabilities provided by a comprehensive MLOps implementation.
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?
Basic Concept: AI-specific threat modeling requires consulting resources that catalogue adversarial attacks specifically developed for AI and ML systems. General cybersecurity frameworks may miss AI-unique attack vectors such as model inversion, data poisoning, and adversarial examples. CompTIA SecAI+ Study Guide identifies MITRE ATLAS as the authoritative source for AI system TTPs.
Why D is Correct: MITRE ATLAS provides a comprehensive, curated knowledge base of adversarial tactics, techniques, and procedures specifically targeting AI and ML systems, derived from real-world attack case studies. Analyzing ATLAS enables the architect to enumerate realistic AI-specific attacks applicable to the system being threat-modeled, which directly answers the question of which attacks can be performed.
Why A is Wrong: Using an LLM to map attack paths introduces uncertainty and potential hallucination risk. LLMs may generate plausible-sounding but inaccurate attack paths and cannot guarantee comprehensive coverage of AI-specific attack techniques.
Why B is Wrong: Quantifying risk of known vulnerabilities is a risk assessment step that occurs after identifying which attacks are possible. The architect must first identify attack possibilities before quantifying their risk impact.
Why C is Wrong: OWASP Top 10 covers web application vulnerabilities and, in its LLM edition, certain LLM-specific risks. However, MITRE ATLAS provides a more comprehensive and structured catalog of AI and ML-specific adversarial TTPs for systematic threat modeling.
Which of the following is the most impactful security risk associated with the use of a generative AI chatbot?
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.
SIMULATION
Instructions: Click the (+) to assign each threat category into its appropriate framework.
An architect is modeling an agentic system to meet security standards.
Basic Concept: This is a Performance-Based Question (PBQ) --- a simulation item requiring interactive drag-and-drop assignment of threat categories to appropriate frameworks in the actual exam. It tests knowledge of how different AI threat frameworks categorize and address specific threat types for agentic systems.
Key Concept --- Framework-to-Threat Mapping: MITRE ATLAS covers ML-specific adversarial tactics such as model evasion, data poisoning, model extraction, and prompt injection for agentic systems. OWASP LLM Top 10 addresses application-level LLM vulnerabilities such as insecure output handling, excessive agency, and supply chain risks. NIST AI RMF addresses governance-level risks across the AI lifecycle. STRIDE addresses architectural threats including spoofing, tampering, repudiation, information disclosure, DoS, and elevation of privilege.
Why This Matters: Agentic AI systems have a unique threat landscape combining traditional software vulnerabilities with AI-specific attacks. Correctly mapping threat categories to frameworks is essential for comprehensive threat modeling of systems that autonomously execute multi-step tasks with tool access and real-world consequences.
A security administrator must provide access controls for AI systems to list tables.
Which of the following should the administrator implement?
Basic Concept: AI systems interact with different resource layers including models, data stores, and infrastructure. Controlling what data an AI system can access requires implementing access controls at the data layer. CompTIA SecAI+ Study Guide differentiates between model access, data access, and network access controls for AI systems.
Why D is Correct: Data access controls govern what data resources an AI system can interact with, including which databases, tables, and records it can read or modify. To control an AI system's ability to list database tables, the administrator must implement data access controls that define precisely which tables the AI can enumerate and query, following the principle of least privilege for data interactions.
Why A is Wrong: Agentic AI access refers to permissions granted to autonomous AI agents to perform actions and use tools. It is a broader concept about what an AI agent can do operationally rather than a specific data-layer access control mechanism.
Why B is Wrong: A Network Access Control List controls network traffic at the IP and port level, determining which hosts can communicate with which network resources. It operates at the network layer and cannot enforce fine-grained control over which database tables an AI system is allowed to list.
Why C is Wrong: Model access controls govern who and what can interact with the AI model itself --- who can query it, update it, or access its parameters. This is distinct from data access, which controls what the model can read from data stores during operation.
126 questions covering all exam domains, starting from $20
Exam domains verified against: Official CompTIA CY0-001 exam guide, last checked August 2026.
Understand core AI principles and terminology including machine learning, deep learning, natural language processing, and automation. Recognize AI applications in security use cases for threat detection and defense, and identify AI-driven threats such as automated phishing, polymorphic malware, and adversarial machine learning.
Sample question from this domain above: Q2
Implement security controls to protect AI systems, data, and models using technical safeguards. Apply best practices for securing AI deployment environments across on-premises, cloud, and hybrid infrastructures. Mitigate adversarial risks and defend against attacks targeting AI models, data pipelines, and inference layers.
Use AI-driven tools to identify anomalies, detect threats, and accelerate incident remediation and response. Automate security workflows by integrating AI for event triage, alert correlation, and response orchestration. Apply AI techniques including threat modeling, behavior analysis, and continuous monitoring in security operations.
Understand global governance requirements and regulatory frameworks applicable to AI adoption. Integrate governance, risk management, and compliance practices throughout the AI lifecycle and projects. Ensure responsible AI use by applying ethical guidelines, legal standards, and industry frameworks such as GDPR and NIST AI RMF.
Sample question from this domain above: Q4
Common questions about the exam itself
