CompTIA · CY0-001
CompTIA SecAI+ Certification (CY0-001) Practice Tests
CompTIA SecAI+ validates the skills needed to secure AI systems, apply AI responsibly within cybersecurity operations, and manage governance, risk, and compliance for AI-enabled environments. It is designed for experienced cybersecurity professionals with 2+ years of hands-on security experience.
Exam Details
Practice Questions
600
≈ 6 practice exams
Duration
60 minutes
Passing Score
600/900
Difficulty
ProfessionalLast Updated
Apr 2026
Topics Covered
Basic AI Concepts for CybersecuritySecuring AI SystemsAI-Assisted Security OperationsAI Governance, Risk, and ComplianceAdversarial AI and Threat MitigationAI Deployment and Security ControlsResponsible AI Practices
CompTIA SecAI+ Certification (CY0-001) Practice Exam Preparation
Use this CY0-001 practice exam to prepare for CompTIA SecAI+ Certification (CY0-001) with realistic questions, detailed explanations, and focused study modes. The practice bank includes 600 questions for CompTIA CY0-001, so you can review the exam steadily instead of relying on one long cram session.
As you practice, pay extra attention to recurring topics such as Basic AI Concepts for Cybersecurity, Securing AI Systems, AI-Assisted Security Operations, AI Governance, Risk, and Compliance, and Adversarial AI and Threat Mitigation. Start with short sessions to identify weak areas, then move into timed quizzes once your accuracy is consistent.
The explanations are especially useful when you want to connect exam wording to the responsibilities and scenarios described in the official certification guidance. Use the free preview first, then unlock the full question bank when you are ready to build a complete study routine.
Exam Domain Breakdown
Basic AI Concepts Related to Cybersecurity17%
Securing AI Systems40%
AI-Assisted Security24%
AI Governance, Risk, and Compliance19%
Exam Overview
CompTIA SecAI+ (CY0-001) is a vendor-neutral professional certification that validates the knowledge and skills required to secure artificial intelligence systems and responsibly integrate AI into cybersecurity operations. Launched on February 17, 2026, it is the first certification in CompTIA's Expansion Series and the first vendor-neutral credential focused specifically on the intersection of AI and cybersecurity. The certification covers implementing technical security controls for AI models, gateways, and data pipelines—including model guardrails, prompt firewalls, encryption requirements, and data anonymization—alongside using AI-driven tools to automate threat detection, anomaly discovery, and incident response workflows.
The exam also addresses AI governance, risk, and compliance (GRC), requiring candidates to apply global regulatory frameworks such as GDPR and the NIST AI Risk Management Framework across the AI lifecycle. Accredited by ANSI to ISO 17024 standards, SecAI+ is valid for three years and requires continuing education units (CEUs) for renewal, following CompTIA's standard certification maintenance model.
Who Should Take This Exam
SecAI+ is designed for experienced cybersecurity practitioners who are integrating AI technologies into their security programs or are responsible for securing AI-enabled environments. Applicable job roles include security engineers, SOC analysts, blue-team operators, application security engineers, and security governance professionals seeking to validate AI-specific competencies alongside their existing security expertise.
CompTIA positions SecAI+ as a mid-level specialization that builds directly on core certifications such as Security+, CySA+, and PenTest+. It is well-suited for professionals already working in security operations, threat detection, or GRC roles who need to demonstrate competence in protecting AI pipelines, applying AI-driven automation, and navigating the compliance requirements of AI-enabled environments.
Prerequisites
There are no formal prerequisites required to sit for the CY0-001 exam. However, CompTIA strongly recommends candidates have 3–4 years of overall IT experience, including at least 2 years of hands-on cybersecurity experience, before attempting SecAI+. Prior attainment of Security+, CySA+, or PenTest+—or equivalent knowledge—is also recommended, as the exam assumes familiarity with core security concepts such as threat modeling, incident response, and risk management.
Candidates should also have a working understanding of foundational AI concepts—including machine learning terminology, model lifecycle basics, and common AI use cases—before diving into the AI-specific controls and governance frameworks that make up the bulk of the exam content. CompTIA positions SecAI+ as an add-on specialization rather than a standalone entry-level credential.
Exam Format
The CY0-001 exam consists of a maximum of 60 questions, combining multiple-choice and performance-based question (PBQ) formats. Performance-based questions require candidates to demonstrate practical skills through simulated scenarios rather than selecting from predefined answers. The total allotted time is 60 minutes, and the exam is delivered in English only.
Scoring is on a scale of 100 to 900, with a passing score of 600. The exam is available through Pearson VUE, which offers both online proctored and in-person testing center delivery options consistent with other CompTIA exams. No specific number of unscored pretest items has been published for this exam version.
Skills Measured
- 1.Domain 1 — Basic AI Concepts Related to Cybersecurity (17%): Covers comparing and contrasting AI types and techniques relevant to security, including Generative AI, Machine Learning, Deep Learning, NLP, LLMs, SLMs, GANs, and Transformers. Candidates must understand core AI principles and terminology and identify AI applications in threat detection, defense automation, and security operations.
- 2.Domain 2 — Securing AI Systems (40%): The largest domain, covering implementation of technical controls to protect AI systems, models, and data. Topics include model controls (model evaluation, guardrails, prompt templates), gateway controls (prompt firewalls, rate/token limits, input quotas), guardrail testing and validation, data security controls (encryption in-transit/at-rest/in-use, anonymization, classification labels, redaction, masking, and minimization), and defenses against adversarial attacks on AI infrastructure.
- 3.Domain 3 — AI-Assisted Security (24%): Focuses on leveraging AI-driven tools to enhance security operations. Candidates must demonstrate ability to use AI for threat detection, anomaly discovery, and incident response automation, as well as operationalizing AI-powered workflows within existing security programs.
- 4.Domain 4 — AI Governance, Risk, and Compliance (19%): Addresses applying global GRC frameworks—including GDPR, NIST AI RMF, and relevant industry standards—to AI-enabled environments. Covers ethical AI practices, legal standards, compliance monitoring, and governance across the full AI system lifecycle.
Study Tips
- Start with CompTIA's official CertMaster Study product (ISBN: 978-1-64274-646-4), which is purpose-built for CY0-001 and maps directly to the published exam objectives. It provides 12 months of access and covers all four domains with adaptive question banks.
- Download and study the official CompTIA SecAI+ Exam Objectives document (Version 1.0/2.0) from the CompTIA website. Map every sub-objective to your study notes, paying special attention to Domain 2 (Securing AI Systems, 40%) since it represents nearly half the exam.
- Use CertMaster Labs (ISBN: 978-1-64274-638-9) to build hands-on familiarity with performance-based question scenarios. PBQs simulate real-world tasks such as configuring prompt firewalls and applying data anonymization controls—skills that cannot be memorized from reading alone.
- Review the NIST AI Risk Management Framework (AI RMF 1.0) and GDPR AI-related provisions directly from their official sources. Domain 4 (GRC, 19%) tests practical application of these frameworks, so understanding their actual structure and terminology is essential.
- Study adversarial AI attack categories—including model inversion, data poisoning, prompt injection, and evasion attacks—using resources like MITRE ATLAS, which maps adversarial machine learning tactics to the MITRE ATT&CK framework and is directly applicable to Domain 2 objectives.
- Use CertMaster Perform (ISBN: 978-1-64274-637-2) in the final two weeks before the exam to simulate timed test conditions. The 60-minute limit is tight for up to 60 questions, so practice pacing yourself across both multiple-choice and performance-based items.
- Leverage the recommended certification pathway (Security+ → CySA+ → SecAI+) as a knowledge scaffold: revisit CySA+ threat detection and security operations content to reinforce the AI-assisted security domain, since Domain 3 builds directly on those analyst-level competencies.
Career Benefits
SecAI+ is positioned at the convergence of two of the fastest-growing areas in enterprise technology, and professionals who hold this credential can demonstrate competency for roles such as AI Security Engineer, Security Operations Analyst, Cloud Security Engineer, AI/ML Security Specialist, and GRC Analyst in AI-enabled organizations. CompTIA's recommended pathway places SecAI+ as a specialization after CySA+ or PenTest+, making it a credential that differentiates mid-career cybersecurity professionals in a crowded market.
The demand for professionals who can both secure AI systems and operationalize AI within security teams is expanding rapidly, with organizations across financial services, healthcare, government, and technology sectors facing mounting AI security incidents and growing regulatory pressure around AI governance. SecAI+ provides a vendor-neutral, ANSI/ISO 17024-accredited credential that signals verified competency to employers regardless of specific technology stack, complementing vendor-specific AI and security certifications from providers such as Microsoft, AWS, and Google.
Sample Questions
5 sample questions with answers and explanations. The full bank has 600 questions, enough for 6 full-length practice exams.
Preview — answers shown1. Woodgrove Bank's fraud detection model shows declining performance. The data science team needs to identify whether the input data distribution has shifted. Which model drift detection methods should they use? (Select two!)
Multiple correct answersAKolmogorov-Smirnov (KS) test to compare feature distributions between training and production data
BAdversarial training to make the model robust to distribution changes
CPopulation Stability Index (PSI) to measure distribution shifts in feature values
DDifferential privacy to protect against membership inference during drift analysis
EHomomorphic encryption to detect drift in encrypted transaction data
Explanation
Kolmogorov-Smirnov (KS) test and Population Stability Index (PSI) are statistical methods for detecting data drift by comparing distributions between training and production data. KS test measures the maximum distance between cumulative distribution functions. PSI quantifies distribution shifts in feature values. Adversarial training improves robustness but doesn't detect drift. Differential privacy protects training data privacy. Homomorphic encryption enables encrypted computation but isn't a drift detection method.
2. Bellows College's security team uses AI-powered threat intelligence tools that analyze external feeds to generate threat assessments. The security manager is concerned about acting on inaccurate AI-generated findings. What is the MOST important validation practice for AI tool outputs used in security decisions? (Select one!)
ACorroborate AI-generated findings against multiple independent data sources and apply human expert judgment before taking consequential action
BSelect AI tools exclusively from the largest vendors because larger organizations produce more accurate models
CDisable all AI-powered threat intelligence tools because their error rate makes them unsuitable for security decisions
DAlways accept AI outputs as authoritative because machine learning models process more data than human analysts can review
Explanation
Corroborating AI-generated findings with independent sources and human expert judgment is critical because AI systems can hallucinate plausible but incorrect threat assessments, reflect training data biases, or lack organizational context needed for accurate evaluation. Security decisions must never rely solely on unvalidated AI outputs. Assuming AI is always authoritative is dangerous — AI systems have well-documented limitations including hallucinations and contextual blindness. Vendor size has no direct correlation with AI accuracy or trustworthiness for specific security use cases. Completely disabling AI threat intelligence tools forfeits valuable capabilities; the correct approach is implementing validation workflows, not avoidance.
3. Blue Yonder Healthcare implements differential privacy for a patient outcome prediction model. The data science team debates the epsilon parameter setting. Epsilon = 0.5 provides strong privacy but 72% accuracy. Epsilon = 8.0 provides 89% accuracy but weaker privacy. Epsilon = 15.0 provides 91% accuracy. What is the correct trade-off assessment? (Select one!)
AEpsilon = 8.0 provides the optimal balance and maximum privacy protection
BHigher epsilon (15.0) provides stronger privacy guarantees and should be selected for healthcare data
CLower epsilon (0.5) provides stronger privacy but lower utility; epsilon above 10 generally provides insufficient privacy protection
DEpsilon values have no impact on privacy; they only affect model accuracy
Explanation
In differential privacy, the epsilon parameter controls the privacy-utility trade-off. Lower epsilon values provide MORE privacy protection but LESS utility (accuracy). Higher epsilon values provide LESS privacy but MORE utility. Epsilon = 0 provides perfect privacy but no utility. Epsilon > 10 is generally considered insufficient for meaningful privacy protection. Therefore, epsilon = 0.5 provides the strongest privacy but lowest accuracy (72%), while epsilon = 15.0 provides weak privacy despite high accuracy (91%). Epsilon = 8.0 is borderline insufficient for privacy protection despite reasonable accuracy. Healthcare applications typically require strong privacy (low epsilon) despite accuracy trade-offs.
4. Tailspin Toys is preparing to deploy an AI system that automatically evaluates job applications, ranks candidates, and generates shortlists for human recruiters to review. The system will be used in hiring decisions across their EU operations. Under the EU AI Act, which classification applies to this system, and what is the primary resulting obligation? (Select one!)
ALimited risk — Tailspin must inform applicants they are interacting with an AI system
BMinimal risk — No mandatory requirements apply; voluntary codes of conduct are recommended
CHigh risk — Tailspin must conduct a conformity assessment, implement risk management, ensure human oversight, and maintain documentation before deployment
DUnacceptable risk — The system is prohibited from deployment in the EU market regardless of safeguards
Explanation
Under the EU AI Act, AI systems used in employment and worker management — specifically including recruitment, candidate screening, and promotion decisions — are explicitly enumerated as high-risk AI applications. This classification applies because such systems significantly impact individuals' fundamental rights and livelihoods. High-risk classification triggers a comprehensive set of mandatory obligations including: a conformity assessment demonstrating compliance before market placement, implementation of a risk management system throughout the system lifecycle, data governance requirements for training data, technical documentation and record-keeping, transparency obligations toward users, human oversight mechanisms ensuring meaningful review of AI decisions, and requirements for accuracy, robustness, and cybersecurity. Limited risk classification applies to AI systems with specific transparency obligations such as chatbots. Minimal risk covers applications like spam filters. Unacceptable risk covers prohibited practices such as social scoring by government authorities or real-time biometric identification in public spaces, neither of which describes this recruitment tool.
5. Northwind implements an ML-based malware classifier that detects polymorphic malware by analyzing behavioral patterns rather than signatures. Which advantage does this approach provide? (Select one!)
ADetection of previously unseen malware variants
BPerfect accuracy with zero false positives
CElimination of adversarial example vulnerabilities
DNo requirement for model updates
Explanation
Behavioral analysis using machine learning can detect polymorphic malware variants that have never been seen before by identifying suspicious patterns and actions, rather than relying on static signatures. This is valuable against rapidly evolving threats. However, ML classifiers are not perfect and will have some false positives. They remain vulnerable to adversarial examples and adversarial malware. Models require regular updates and retraining as attacker techniques evolve and concept drift occurs.
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