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100+ Free EXIN AI Security Professional Practice Questions

Prepare for the EXIN AI Security Professional based on the OWASP AI Exchange exam with instant access — no signup required.

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Key Facts: EXIN AI Security Professional Exam

40 MCQs / 90 min

Exam Structure

EXIN

65% (26/40)

Passing Score

EXIN

€225 / $265

Exam Fee

EXIN

4 Domains

Domain Breakdown

EXIN / OWASP AI Exchange

OWASP AI

Underlying Framework

OWASP AI Exchange

Closed-book

Testing Style

EXIN

The EXIN AI Security Professional exam consists of 40 multiple-choice questions to be completed in 90 minutes. A passing score of 65% (26/40) is required. The exam tests four key areas: OWASP AI Exchange & AI Threat Taxonomy (25%), Data & Model Security (25%), Secure AI System Design & Supply Chain (25%), and Testing, Governance & Incident Response (25%).

Sample EXIN AI Security Professional Practice Questions

Try these sample questions to test your EXIN AI Security Professional exam readiness. Each question includes a detailed explanation. Start the interactive quiz above for the full 100+ question experience with AI tutoring.

1What is the primary objective of the OWASP AI Exchange framework in artificial intelligence security?
A.Providing a structured threat matrix and control mapping across the entire AI lifecycle to mitigate AI-specific risks
B.Replacing traditional ISO 27001 information security controls with AI-only algorithms
C.Defining copyright licensing standards for open-source machine learning models
D.Standardising hardware benchmarks for GPU and TPU training clusters
Explanation: The OWASP AI Exchange provides a comprehensive, collaborative open-source framework mapping threats, attack surfaces, and security controls across all stages of the AI lifecycle. It serves as a unified reference for threat modeling and securing machine learning and generative AI implementations.
2In the context of artificial intelligence, how is a loss of confidentiality primarily manifested according to the OWASP AI Exchange?
A.Unauthorised modification of hyperparameter configuration files during training
B.Exposing sensitive training data records or proprietary model weights through inference attacks
C.Forcing the AI system to generate slow responses via resource exhaustion prompts
D.Altering prediction outputs so that legitimate users receive inaccurate results
Explanation: Confidentiality breaches in AI specifically involve revealing protected information embedded within the model, such as private training data (via inversion/extraction) or proprietary model architectures and weights (via model theft).
3Which scenario best illustrates an integrity attack on a machine learning model?
A.An attacker extracting confidential patient records by querying a medical diagnosis model
B.An attacker sending flood traffic to an LLM endpoint to cause service downtime
C.An attacker injecting crafted samples into the training dataset to corrupt the model's decision boundaries
D.An attacker sniffing unencrypted network traffic between the model server and database
Explanation: Data poisoning or input manipulation that corrupts model decision boundaries or alters model predictions directly compromises system integrity by impairing the trustworthiness of outputs.
4What is the main security risk regarding availability when deploying Large Language Models (LLMs) in production?
A.Denial of Service (DoS) via resource exhaustion caused by computationally expensive prompts or infinite processing loops
B.Unauthorised extraction of system prompt instructions by external users
C.Inability to log user IP addresses during active session connections
D.Loss of backup model files stored in offline tape archives
Explanation: LLMs consume significant GPU and memory resources. Attackers can craft complex, long-context, or recursively expanded prompts (sponge attacks) that consume maximum compute, degrading or completely stopping service availability for legitimate users.
5What is a fundamental difference between traditional software security threat modeling and AI threat modeling?
A.Traditional software does not require access control mechanisms, whereas AI software does
B.AI systems are entirely immune to traditional vulnerabilities like SQL injection and buffer overflows
C.Traditional software threat modeling only applies to desktop applications, not cloud services
D.AI systems introduce non-deterministic model behavior, data dependencies, and probabilistic decision boundaries alongside traditional software code risks
Explanation: Traditional threat modeling focuses on deterministic code execution, logic flaws, and static input validation. AI threat modeling must account for data quality, model weights, probabilistic outputs, non-deterministic behavior, and adversarial input perturbations.
6Which asset category in an AI system architecture represents the learned parameters after model training?
A.Training dataset metadata
B.Model weights and biases
C.Feature extraction scripts
D.Inference API wrapper code
Explanation: Model weights and biases represent the numerical values learned by neural networks during training. They contain the core intelligence, intellectual property, and statistical patterns derived from data.
7In the OWASP AI Exchange pipeline view, what is the role of the feature engineering component?
A.Serving final model prediction tokens to external REST clients
B.Evaluating model accuracy against held-out test benchmarks
C.Transforming raw input data into numerical representations suitable for model consumption
D.Encrypting model parameters while stored at rest on disk
Explanation: Feature engineering transforms raw unstructured or structured data (text, images, audio, tabular) into cleaned, normalized numerical tensors or vectors required by machine learning algorithms.
8What is the primary benefit of conducting AI asset mapping during an initial security assessment?
A.Establishing a complete inventory of datasets, models, code dependencies, and infrastructure to accurately define attack surfaces
B.Ensuring that all model training scripts run 50% faster in production environments
C.Eliminating the need for periodic vulnerability scanning across cloud infrastructure
D.Automatically fixing code bugs in third-party Python machine learning libraries
Explanation: Asset mapping identifies all components across data ingestion, preprocessing, training pipelines, model registries, and inference endpoints, enabling security teams to systematically assess threats and apply targeted controls.
9An enterprise integrates a pre-trained open-source foundational model downloaded from an unverified public repository into its customer support application. How does the OWASP AI Exchange classify this primary risk?
A.Internal insider threat
B.AI supply chain asset risk
C.Network layer denial of service
D.Physical facility security breach
Explanation: Using unverified third-party models introduces supply chain risk, including potential embedded backdoors, malicious code in serialization formats (such as pickle execution), and hidden biases.
10How does the STRIDE-AI methodology adapt the traditional STRIDE threat model specifically for artificial intelligence architectures?
A.By removing Spoofing and Tampering threats because AI systems use cryptographic tokens
B.By focusing exclusively on hardware temperature monitoring in GPU clusters
C.By replacing all threat categories with a single GDPR compliance checklist
D.By expanding existing threat categories to incorporate data manipulation, model evasion, prompt injection, and model inversion
Explanation: STRIDE-AI adapts Spoofing, Tampering, Repudiation, Information Disclosure, Denial of Service, and Elevation of Privilege to cover AI-specific attack vectors like prompt spoofing, data tampering/poisoning, model inversion disclosure, and agent privilege escalation.

About the EXIN AI Security Professional Exam

The EXIN AI Security Professional certification validates your ability to assess, design, implement, and govern security controls for artificial intelligence and machine learning applications. Grounded in the OWASP AI Exchange, it covers AI threat modeling, data poisoning, prompt injection defenses, supply chain security, and AI regulatory compliance.

Questions

40 scored questions

Time Limit

90 minutes

Passing Score

65% (26/40)

Exam Fee

€225 / $265 (EXIN)

EXIN AI Security Professional Exam Content Outline

25%

OWASP AI Exchange & AI Threat Taxonomy

OWASP AI Exchange matrix, threat categories, AI asset mapping, attack surfaces, and risk assessment methodologies for AI/ML.

25%

Data & Model Security

Data poisoning, training data confidentiality, model inversion, membership inference, direct/indirect prompt injection, and model theft.

25%

Secure AI System Design, Supply Chain & Training Security

Architectural controls, secure MLOps pipelines, third-party model and data provenance, model signing, hardware isolation, and training security.

25%

Testing, Governance, Verification & Incident Response

Red teaming LLMs/ML models, automated security testing, AI governance frameworks (EU AI Act, NIST AI RMF), continuous monitoring, and AI incident response.

How to Pass the EXIN AI Security Professional Exam

What You Need to Know

  • Passing score: 65% (26/40)
  • Exam length: 40 questions
  • Time limit: 90 minutes
  • Exam fee: €225 / $265

Keys to Passing

  • Complete 500+ practice questions
  • Score 80%+ consistently before scheduling
  • Focus on highest-weighted sections
  • Use our AI tutor for tough concepts

EXIN AI Security Professional Study Tips from Top Performers

1Familiarise yourself thoroughly with the OWASP AI Exchange threat taxonomy and matrix structure.
2Understand the operational mechanics and mitigations for direct and indirect prompt injection attacks.
3Learn the differences between data poisoning (clean-label vs backdoor attacks) and model inversion/extraction attacks.
4Study AI supply chain risk controls, including AI Bill of Materials (AIBOM), model provenance, and secure model registries.
5Review regulatory and governance standards including the EU AI Act risk tiers and the NIST AI Risk Management Framework (AI RMF 1.0).

Frequently Asked Questions

What is the EXIN AI Security Professional exam format?

The EXIN AI Security Professional exam consists of 40 multiple-choice questions with a 90-minute time limit. It is a closed-book examination. To pass, candidates must score at least 65% (26 correct answers out of 40).

What is the OWASP AI Exchange and how does it relate to this exam?

The OWASP AI Exchange is a comprehensive open-source threat matrix and security control framework for AI systems. The EXIN AI Security Professional exam directly aligns with the OWASP AI Exchange framework, assessing candidates on threat identification, attack vectors, and practical defense measures for machine learning and LLM applications.

What topics are covered in the EXIN AI Security Professional exam?

The exam is divided equally across four 25% domains: (1) OWASP AI Exchange & AI Threat Taxonomy, (2) Data & Model Security (poisoning, inversion, prompt injection), (3) Secure AI System Design, Supply Chain & Training Security, and (4) Testing, Governance, Verification & Incident Response.

How much does the EXIN AI Security Professional exam cost?

The official EXIN exam fee is approximately €225 / $265. Pricing may vary slightly depending on region and exam delivery provider.

Are there any prerequisites for taking the EXIN AI Security Professional exam?

There are no formal strict prerequisites, though candidates are strongly advised to possess general cybersecurity knowledge and a solid understanding of machine learning concepts and lifecycle workflows.

How can I prepare for the EXIN AI Security Professional exam?

Preparation should focus on studying the OWASP AI Exchange document, understanding technical attack vectors like prompt injection and data poisoning, reviewing AI governance frameworks (such as the NIST AI RMF and EU AI Act), and completing practice question sets.