Detailed Exam Domain Coverage

This practice test course maps directly to the official CompTIA SecAI+ (CY0-001) V1 exam blueprint. Every question is aligned with the following domains and specific exam sub-topics:

Basic AI Concepts Related to Cybersecurity (17%)
- Topics Covered: Types of AI architectures (generative AI, machine learning, statistical learning, transformers); Core AI principles and terminology (ML, DL, NLP, automation); AI applications in modern security infrastructure (threat detection, defensive strategies, SOC operations); AI-driven threats (automated phishing, adversarial ML, malicious generative AI tools); Comparing and contrasting AI techniques used in cyber defense.
Securing AI Systems (40%)
- Topics Covered: Implementing robust security controls for AI models and data pipelines; Securing AI deployment environments across on-premises, cloud, and hybrid architectures; Mitigating adversarial risks to models, training data, and inference layers; Deploying model-level controls (evaluation frameworks, safety guardrails, prompt templates, rate limits); Supply-chain validation and protection of AI training datasets.
AI-Assisted Security (24%)
- Topics Covered: Deploying AI-driven tools to identify anomalies and flag hidden threats; Automating complex security workflows (event triage, alert correlation, response orchestration); Applying machine learning techniques to threat modeling and behavior analysis; Integrating AI layers for advanced log enrichment and automated incident response; Leveraging AI to accelerate remediation timelines and sharpen detection accuracy.
AI Governance, Risk, and Compliance (19%)
- Topics Covered: Aligning enterprise AI projects with the NIST AI RMF, GDPR, and global regulatory frameworks; Addressing critical AI bias, transparency, explainability, and auditability requirements; Implementing specialized GRC controls for AI systems and underlying data pools; Identifying and managing emerging AI-driven risks and compliance obligations; Developing corporate policies for responsible AI usage and ethical organizational considerations.

Course Description

Passing the CompTIA SecAI+ (CY0-001) V1 exam requires more than just memorizing definitions; you need to understand how AI models function, how attackers exploit them, and how to use AI tools defensively in a modern Security Operations Center (SOC). I built this practice test course to bridge the gap between theoretical knowledge and exam-day reality.

Instead of generic, AI-generated questions that don't match the actual exam format, these mock exams are crafted from scratch to mirror the specific tone, difficulty, and situational context used by CompTIA. Each question tests your engineering mindset, requiring you to evaluate deployments, secure data pipelines, mitigate risks like prompt injection or data poisoning, and apply frameworks like the NIST AI RMF to real-world scenarios.

What sets this course apart is the depth of the explanations. I don’t just tell you which answer is right. I break down all six options for every single question, explaining exactly why the correct choice wins and why the other five options fail under the given scenario. This approach transforms a simple practice test into a highly efficient study guide, helping you identify knowledge gaps, eliminate traps, and build the confidence needed to clear your exam on the first attempt.

Sample Questions Preview

Question 1: Securing AI Systems

An engineer wants to prevent prompt injection attacks where malicious users bypass the safety layers of a customer-facing Large Language Model (LLM). Which control is most effective to implement at the inference layer?

A. Inputs/Outputs Guardrails
B. Training Data Poisoning Filters
C. Homomorphic Encryption
D. Model Fine-Tuning via RLHF
E. Rate Limiting based on IP address
F. Supply-chain signature validation

Correct Answer: A

Explanation of options:
- A is Correct: Inputs/Outputs Guardrails act as a validation layer directly at the inference phase. They inspect user prompts before they reach the model (to catch injection attempts) and evaluate the model's output before it reaches the user (to catch leaks or jailbreaks).
- B is Incorrect: Data poisoning filters protect the training data phase from malicious manipulation, not the real-time inference layer where user-driven prompt injections occur.
- C is Incorrect: Homomorphic encryption allows computation on encrypted data, protecting data privacy during processing, but it does not evaluate or stop the semantic content of a malicious prompt injection.
- D is Incorrect: While Reinforcement Learning from Human Feedback (RLHF) helps align model behavior safely during training, it is not a direct runtime control at the inference layer to stop creative, novel injection attacks.
- E is Incorrect: Rate limiting prevents Denial of Service (DoS) attacks or automated API abuse by limiting request volume, but it cannot analyze or stop a single, well-crafted malicious prompt injection string.
- F is Incorrect: Supply-chain signature validation ensures the integrity of third-party models or software components before deployment, but it offers zero protection against runtime attacks initiated by end-users.

Question 2: AI-Assisted Security

A Security Operations Center (SOC) team wants to accelerate remediation and minimize alert fatigue caused by high-volume, repetitive log data coming from multiple cloud environments. Which AI-assisted workflow optimization should they prioritize?

A. Automated event triage and alert correlation via machine learning models
B. Static signature-based regex matching rules
C. Manual threat modeling using the STRIDE methodology
D. Adversarial retraining of edge firewalls
E. Symmetric key distribution encryption
F. Data masking at the storage layer

Correct Answer: A

Explanation of options:
- A is Correct: Machine learning models excel at processing massive datasets, recognizing patterns across disparate logs, correlating related alerts into a single incident, and triaging priority levels. This directly reduces alert fatigue and speeds up response times.
- B is Incorrect: Static signature-based regex rules are rigid and brittle. They fail to scale across complex cloud environments and often increase alert fatigue by triggering false positives on variant data patterns.
- C is Incorrect: STRIDE is an excellent framework for threat modeling during the software development design phase, but it is a manual process that cannot optimize live SOC workflows or automate real-time alert triage.
- D is Incorrect: Adversarial retraining hardens models against specific exploits, but it does not address log ingestion, alert correlation, or daily SOC workflow optimization.
- E is Incorrect: Symmetric key encryption secures data in transit or at rest, but it has no impact on analyzing logs, grouping alerts, or optimizing security operations workflows.
- F is Incorrect: Data masking protects sensitive information (like PII) within storage systems for privacy compliance, but it does not assist in triaging alerts or speeding up incident remediation.

Question 3: AI Governance, Risk, and Compliance

An organization is aligning its newly deployed generative AI human resources system with the NIST AI Risk Management Framework (AI RMF). They need to ensure internal auditors and external stakeholders can trace exactly how the system scores and recommends automated hiring decisions. Which specific GRC requirement are they addressing?

A. Transparency and auditability
B. Data pipeline homomorphic isolation
C. Model rate limiting enforcement
D. Automated patch management
E. Semantic supply-chain validation
F. Network boundary fuzzing

Correct Answer: A

Explanation of options:
- A is Correct: Transparency and auditability center on the ability to trace, explain, and review an AI system's decision-making process. This ensures accountability, helps detect underlying bias, and aligns directly with compliance mandates within the NIST AI RMF.
- B is Incorrect: Homomorphic isolation is a technical data privacy concept; it does not solve the governance problem of making a model's internal logic explainable or auditable to stakeholders.
- C is Incorrect: Rate limiting is an operational security tool used to prevent resource exhaustion or scraping; it does not provide insight into how a model arrives at its specific outputs.
- D is Incorrect: Patch management handles software vulnerabilities across systems, which is a standard security practice rather than a specialized governance framework for AI explainability.
- E is Incorrect: Supply-chain validation checks the integrity of third-party software components but does not address the operational transparency or tracing of the internal logic of a custom HR model.
- F is Incorrect: Fuzzing is an active security testing technique used to find software bugs or input vulnerabilities by injecting malformed data; it does not contribute to regulatory compliance transparency or system auditability.

Course Highlights

Welcome to the Mock Exam Practice Tests Academy to help you prepare for your CompTIA SecAI+ Certification course.
You can retake the exams as many times as you want
This is a huge original question bank
You get support from instructors if you have questions
Each question has a detailed explanation
Mobile-compatible with the Udemy app

I hope that by now you're convinced! And there are a lot more questions inside the course.