CompTIA SecAI+ CY0-001 | Practice Exams 2026 | 900 Questions

Are You Ready to Prove You Can Secure the Future of AI?Artificial intelligence is transforming cybersecurity — and with it, the expectations placed on security professionals. Organisations worldwide are deploying AI-powered tools, large language models, and automated threat systems at an unprecedented pace. But AI introduces a new class of risk: prompt injection attacks, model poisoning, data leakage, adversarial manipulation, and governance challenges that traditional security frameworks were never designed to handle.CompTIA's brand-new SecAI+ (CY0-001) certification validates that you understand how to secure AI systems, leverage AI for defence, and govern AI responsibly within an enterprise environment. It is one of the first vendor-neutral certifications built specifically at the intersection of artificial intelligence and cybersecurity — and earning it signals to employers that you are equipped to protect the next generation of technology infrastructure.This course gives you the most comprehensive practice exam preparation available — 900 expertly crafted questions across 6 full-length practice tests — designed to build your confidence, sharpen your critical thinking, and ensure you walk into exam day fully prepared.What Is This Course?This is a dedicated practice exam course for the CompTIA SecAI+ CY0-001 (Version 1) certification. It is not a lecture series or a video course. It is a focused, exam-simulation experience designed to:Test your knowledge across all four official exam domainsReveal knowledge gaps before you sit the real examBuild exam stamina by exposing you to realistic question volume and complexityReinforce learning through detailed, premium-quality explanations for every single answer optionYou will receive 6 complete practice exams, each containing 150 questions, for a total of 900 unique practice questions. Every question is mapped to the official CY0-001 exam objectives and weighted to match the real exam's domain distribution.Who Is This Course For?This course is built for anyone preparing to take — and pass — the CompTIA SecAI+ certification exam, including:IT professionals expanding their skill set into AI securityCybersecurity analysts and engineers who need to understand AI-specific threats and controlsSecurity architects designing or evaluating AI deploymentsGRC professionals responsible for AI governance, risk assessment, and complianceCareer changers entering cybersecurity through the AI security pathwayCertification candidates who have completed their study material and want rigorous exam-level practice before test daySOC analysts, penetration testers, and incident responders who encounter AI tools and AI-generated threats in their daily workWhether you are an experienced security professional or someone transitioning into the field, these practice exams will stress-test your readiness and identify exactly where to focus your remaining study time.What Will You Learn?By working through all 900 questions and studying the detailed explanations, you will build and validate competence in:Understanding core AI concepts — including machine learning, deep learning, transformers, GANs, NLP, LLMs, SLMs, and training techniques — within a cybersecurity contextApplying prompt engineering principles and recognising the security implications of system prompts, user prompts, and prompt templatesProtecting training data through proper data lineage, provenance, cleansing, verification, augmentation, and balancingImplementing retrieval-augmented generation (RAG) securely, including vector storage and embedding protectionSecuring every phase of the AI lifecycle — from business use case alignment through deployment, monitoring, and iterationUsing AI threat-modelling resources including the OWASP LLM Top 10, OWASP ML Security Top 10, MITRE ATLAS, MIT AI Risk Repository, and CVE AI Working GroupImplementing security controls such as model guardrails, prompt firewalls, rate limits, token limits, input quotas, modality limits, and endpoint access controlsEnforcing access controls across model, data, agent, and network/API layersApplying data security controls — encryption in transit, at rest, and in use; data anonymisation; classification labels; redaction; masking; and minimisationConfiguring monitoring and auditing for AI systems — prompt monitoring, log sanitisation, log protection, confidence scoring, bias auditing, hallucination detection, and AI cost monitoringAnalysing evidence of AI-specific attacks — prompt injection, model poisoning, data poisoning, jailbreaking, input manipulation, model inversion, model theft, membership inference, AI supply chain attacks, transfer learning attacks, model skewing, output integrity attacks, backdoor attacks, Trojan attacks, insecure output handling, model denial of service, excessive agency, and overrelianceRecommending compensating controls appropriate to each attack typeUsing AI-enabled security tools — IDE plug-ins, browser plug-ins, CLI plug-ins, chatbots, personal assistants, and Model Context Protocol (MCP) servers — for tasks including vulnerability analysis, anomaly detection, automated penetration testing, incident management, and threat modellingUnderstanding how AI enables and enhances attack vectors — deepfakes, impersonation, social engineering, reconnaissance, obfuscation, automated malware generation, and DDoSAutomating security tasks with AI agents, scripting tools (low-code/no-code), CI/CD integration, software composition analysis, and automated deployment/rollbackExplaining organisational governance structures for AI — AI Centre of Excellence models, AI-related roles (data scientist, AI architect, ML engineer, AI security architect, AI governance engineer, AI auditor, and others), and AI policies and proceduresEvaluating risks associated with AI — fairness, reliability, transparency, differential privacy, explainability, inclusiveness, accountability, intellectual property risks, autonomous system risks, and shadow AINavigating compliance frameworks — EU AI Act, OECD standards, ISO AI standards, NIST AI Risk Management Framework (AIRMF), corporate policies (sanctioned versus unsanctioned AI, private versus public models, sensitive data governance), third-party compliance evaluations, and data sovereigntyOfficial Exam Information — CompTIA SecAI+ CY0-001 - V1Understanding the exam structure is essential for effective preparation. Here are the key details:Exam Number: CY0-001 V1Number of Questions: Maximum of 60Question Types: Multiple-choice and performance-basedExam Duration: 60 minutesPassing Score: 600 (on a scale of 100–900)Recommended Experience: 3–4 years of IT experience and approximately 2 years of hands-on cybersecurity experienceExam Domains and Weighting:1.0 Basic AI Concepts Related to Cybersecurity — 17%2.0 Securing AI Systems — 40%3.0 AI-assisted Security — 24%4.0 AI Governance, Risk, and Compliance — 19%Each of the 6 practice exams in this course mirrors this exact domain distribution, ensuring that the volume and emphasis of your practice accurately reflects what you will face on exam day.Why This Practice Exam Course Is ValuablePassing a CompTIA certification exam is not simply about memorising facts. It requires the ability to analyse scenarios, evaluate trade-offs, and select the best course of action under time pressure. That is exactly the skill set these practice exams are designed to develop.Here is what sets this course apart:900 unique, scenario-based questions. No filler. No recycled question stems. No trivial recall items. Every question is written to challenge your ability to apply knowledge — not just remember it.Exact domain weighting in every practice exam. Each 150-question test allocates questions precisely according to the official blueprint: 26 questions for Domain 1 (17%), 60 questions for Domain 2 (40%), 36 questions for Domain 3 (24%), and 28 questions for Domain 4 (19%).Premium-depth explanations for every answer option. This is not a course where you see "A is correct" and nothing else. Every correct answer includes a detailed explanation of 6–10 sentences covering the security reasoning, risk implications, objective alignment, and enterprise context. Every incorrect answer includes 3–6 sentences explaining precisely why it is wrong, what misconception it targets, and how it contrasts with the correct approach.Calibrated difficulty distribution. Each practice exam includes approximately 20% easy questions, 50% moderate questions, and 30% challenging questions — reflecting the range of difficulty you should expect on the actual exam. Challenging questions involve multi-layer AI attack analysis, threat-model mapping, control trade-off decisions, data governance evaluation, and compliance scenario analysis.Complete uniqueness across all 6 sets. No prompt injection scenario is repeated across exams. No guardrail storyline is recycled. No compliance case study is reworded and reused. Each of the 6 practice tests presents entirely fresh scenarios and contexts.Skills Covered in This CourseThe questions in this course cover the full breadth of the CompTIA SecAI+ CY0-001 exam objectives, including:AI types and techniques — generative AI, machine learning, statistical learning, transformers, deep learning, GANs, NLP, LLMs, and SLMsModel training techniques — supervised learning, unsupervised learning, reinforcement learning, federated learning, fine-tuning, epochs, pruning, and quantisationPrompt engineering — system prompts, user prompts, zero-shot, one-shot, multi-shot prompting, system roles, and templatesData security for AI — data cleansing, verification, lineage, integrity, provenance, augmentation, balancing, watermarking, RAG, vector storage, and embeddingsAI lifecycle security — business use case alignment, data collection, preparation, model development, evaluation, deployment, validation, monitoring, feedback, and human-centric design principlesAI threat modelling — OWASP LLM Top 10, OWASP ML Security Top 10, MITRE ATLAS, MIT AI Risk Repository, CVE AI Working Group, and threat-modelling frameworksSecurity controls for AI — model evaluation, model guardrails, prompt templates, prompt firewalls, rate limits, token limits, input quotas, modality limits, endpoint access controls, and guardrail testingAccess controls — model access, data access, agent access, and API accessData security controls — encryption (in transit, at rest, in use), anonymisation, classification labels, redaction, masking, and minimisationAI monitoring and auditing — prompt monitoring, log monitoring, log sanitisation, log protection, response confidence levels, rate monitoring, AI cost monitoring, hallucination detection, accuracy auditing, bias and fairness assessment, and access auditingAI attack analysis — prompt injection, model poisoning, data poisoning, jailbreaking, input manipulation, backdoor attacks, Trojan attacks, model inversion, model theft, membership inference, AI supply chain attacks, transfer learning attacks, model skewing, output integrity attacks, insecure output handling, model DoS, sensitive information disclosure, insecure plug-in design, excessive agency, overreliance, and circumventing AI guardrailsCompensating controls — prompt firewalls, model guardrails, access controls, data integrity controls, encryption, prompt templates, rate limiting, and least privilegeAI-enabled security tools — IDE plug-ins, browser plug-ins, CLI plug-ins, chatbots, personal assistants, and MCP servers for signature matching, code quality, vulnerability analysis, automated pen testing, anomaly detection, pattern recognition, incident management, threat modelling, fraud detection, translation, and summarisationAI-enhanced attack vectors — deepfakes, impersonation, misinformation, disinformation, adversarial networks, reconnaissance, social engineering, obfuscation, automated data correlation, and automated attack generationSecurity automation with AI — scripting tools (low-code/no-code), document synthesis, incident response ticket management, change management, AI agents, and CI/CD integration (code scanning, SCA, unit testing, regression testing, model testing, automated deployment/rollback)AI governance — AI Centre of Excellence, AI policies and procedures, and AI-related rolesAI risk — responsible AI principles (fairness, reliability, safety, transparency, privacy, security, differential privacy, explainability, inclusiveness, accountability, consistency, awareness training), bias, data leakage, reputational loss, model performance risks, IP risks, autonomous system risks, and shadow AIAI compliance — EU AI Act, OECD standards, ISO AI standards, NIST AIRMF, corporate policies, third-party compliance evaluations, and data sovereigntyHow the Practice Questions Are DesignedEvery question in this course is crafted following professional exam design principles:Scenario-driven format. The majority of questions present a realistic enterprise scenario — a security team responding to an incident, an architect designing a deployment, a governance committee evaluating risk — and then ask you to identify the most appropriate response. This mirrors the decisio