ISTQB AI Testing (CT-AI) Mock Tests - 240 Questions - 2026
Course Description
Are you preparing for the ISTQB Certified Tester - Testing with Generative AI (CT-GenAI) certification and want to assess your readiness with realistic, high-quality exam-style practice questions?
This comprehensive practice exam course has been designed to mirror the real CT-GenAI certification exam as closely as possible..
With 6 full-length practice tests containing 240 questions in total, you will gain the confidence and knowledge required to pass the ISTQB CT-GenAI certification on your very first attempt. Each question is carefully written to match the difficulty, structure, and exam-style wording you will face on test day.
Every question comes with detailed explanations for both correct and incorrect answers, ensuring that you not only know the right answer but also understand why the other options are wrong. This unique approach deepens your understanding and prepares you for any variation of the question that may appear in the real exam.
Our ISTQB CT-learn istqb testing genai ct genai practice exams 240 questions will help you identify your strong areas and pinpoint where you need improvement. By completing these tests under timed conditions, you will build the exam discipline and confidence required to succeed.
This course is updated to stay 100% aligned with the latest ISTQB CT-GenAI v1.0 syllabus (2025 release).
Comprehensive Coverage
This comprehensive practice exam course is designed to help AI testers, QA engineers, developers, and professionals assess readiness, reinforce concepts, and master the ISTQB CT-AI certification.
Each mock test is carefully crafted to cover 100% of the official syllabus, including: AI fundamentals, ML workflows, Neural networks, Bias, ethics, transparency, explainability (XAI), AI test automation, Overfitting/underfitting, Data preparation, Dataset management, Scenario-based testing, and AI lifecycle strategies.
This course is regularly updated to stay 100% aligned with ISTQB evolving AI concepts and knowledge levels.
Why This ISTQB CT-AI Practice Exam Course is Unique
6 Full-Length Mock Exams: Total 240+ questions simulating the real ISTQB CT-AI exam structure.
100% Syllabus Coverage: Covers all K-level topics from K1 (Remember) to K4 (Analyze) from official syllabus.
Diverse Question Categories: This course ensures comprehensive preparation across all ISTQB CT-AI knowledge levels, aligning with the official syllabus:
K1 – Remember: Recall key facts, definitions, and AI/ML terminology.
K2 – Understand: Explain and interpret AI testing concepts, ML workflows, and quality characteristics.
K3 – Apply: Use AI testing principles and methods in practical, real-world scenarios.
K4 – Analyze: Break down complex AI systems to identify biases, errors, model drift, and relationships.
Real Exam-Like Format: Multiple-choice and select-all-that-apply questions with balanced answer distribution.
Comprehensive Explanations: Each question includes detailed rationales for all answer options, helping you learn why answers are correct or incorrect.
Latest Syllabus Alignment: Topics include AI fundamentals, ML workflow, neural networks, bias, ethics, XAI, AI test automation, and AI system lifecycle.
Every question is mapped to its relevant domain or chapter, helping learners track syllabus coverage effectively.
Scenario-Based Questions: Real-world, practical examples replicating ISTQB CT-AI exam conditions.
Exam Weightage Distribution: Questions follow official topic weightage for strategic preparation.
Timed Practice: Simulate realistic exam durations for time management and confidence.
Ideal for AI Testers & QA Engineers: Build skills for ISTQB certification and real-world AI testing.
Randomized Question Bank: Questions and options reshuffle in each attempt to prevent memorization and encourage active learning.
Performance Analytics: Receive domain-wise insights to identify strengths and improvement areas, focusing preparation on topics like Responsible AI, Model Deployment, or Prompt Engineering.
Practical, Real-World Application: Reinforce knowledge through scenario-based and problem-solving questions across all syllabus topics.
Exam Details – ISTQB Certified Tester – AI Testing (CT-AI) Exam Details
Exam Body: ISTQB (International Software Testing Qualifications Board)
Exam Name: ISTQB Certified Tester – AI Testing (CT-AI)
Prerequisite Certification: ISTQB free en istqb certified tester learn istqb foundation level ctfl mock exams 240 questions 2026 v4 0 1 course Level (CTFL)
Exam Format: Multiple Choice Questions (MCQs) – single and multiple-select questions
Certification Validity: Lifetime (no renewal required)
Number of Questions: 40
Total Points: 47 points
Passing Score: 31 points out of 47 points (≈65%)
Exam Duration: 60 minutes (75 minutes for non-native English speakers)
Question Weightage: Varies (some questions carry 1 point, some 2 points)
Language: English (localized versions may be available)
Exam Availability: Online proctored exam or in test centers (depending on region)
Detailed Syllabus and Topic Weightage
The ISTQB CT-AI certification exam evaluates your understanding of AI testing principles, machine learning testing, quality characteristics, AI test automation, and practical application of testing AI-based systems. The syllabus is divided into 11 Domains, covering knowledge levels K1–K4, with question distribution reflecting topic weightage.
Domain1: Introduction to AI (~10–12%)
AI definitions, AI effect, narrow/general/super AI
AI vs. conventional systems
AI technologies, frameworks, and hardware
AI as a Service (AlaaS), pre-trained models, transfer learning
Standards and regulations (e.g., GDPR, ISO)
Domain 2: Quality Characteristics for AI-Based Systems (~10–12%)
Flexibility, adaptability, autonomy, evolution
Bias: algorithmic, sample, inappropriate
Ethics, side effects, reward hacking
Transparency, interpretability, explainability (XAI)
Safety in AI systems
Domain 3: Machine Learning (ML) Overview (~8–10%)
Supervised, unsupervised, reinforcement learning
ML workflow: training, evaluation, tuning, testing
Algorithm selection factors
Overfitting and underfitting
Domain 4: ML Data (~8–10%)
Data preparation: acquisition, preprocessing, feature engineering
Training, validation, and test datasets
Data quality issues and their impact
Data labeling approaches and mislabeling causes
Domain 5: ML Functional Performance Metrics (~6–8%)
Confusion matrix, accuracy, precision, recall, F1-score
ROC curve, AUC, MSE, R-squared, silhouette coefficient
Limitations and selection of metrics
Benchmark suites (e.g., MLCommons)
Domain 6: ML Neural Networks and Testing (~6–8%)
Structure and function of neural networks and DNNs
Coverage measures: neuron, threshold, sign-change, value-change, sign-sign
Domain 7: Testing AI-Based Systems Overview (~10–12%)
Specification challenges
Test levels: input data, model, component, integration, system, acceptance
Test data challenges, automation bias, concept drift
Documentation and test approach selection
Domain 8: Testing AI-Specific Quality Characteristics (~8–10%)
Self-learning, autonomous, probabilistic, complex systems
Testing for bias, transparency, interpretability, explainability
Test oracles and acceptance criteria
Domain 9: Methods and Techniques for Testing AI-Based Systems (~10–12%)
Adversarial attacks, data poisoning
Pairwise, back-to-back, A/B, metamorphic testing
Experience-based and exploratory testing
Test technique selection
Domain 10: Test Environments for AI-Based Systems (~4–6%)
Unique test environment needs
Benefits of virtual test environments
Domain 11: Using AI for Testing (~4–6%)
AI technologies in testing
Defect analysis, test case generation, regression optimization
Defect prediction
GUI testing with AI
ISTQB CT-AI Exam Categories and Weightage
The 40-question ISTQB CT-AI exam (total 47 points) is divided into three main categories to evaluate different levels of learning and application in AI testing:
Foundational (K1–K2):
Domains 1, 2, 6, 7, and 10
Worth 12 points (~26% of the exam)
Focuses on basic AI concepts, quality characteristics, testing fundamentals, and recalling key definitions
Applied (K2–K3, H1–H2):
Domains 3, 4, 5, and 11
Worth 23 points (~49% of the exam)
Tests ability to apply knowledge in free sap s 4hana co practice tests practical scenarios course, including data preparation, ML metrics, AI testing methods, and using AI in testing workflows
Analytical (K3–K4, H2):
Domains 8 and 9
Worth 12 points (~25% of the exam)
Evaluates ability to analyze AI test strategies, identify bias, and assess explainability (XAI) in AI systems
ISTQB CT-AI Exam K-Level Distribution
K1 – Remember: Each question is worth 1 point, ~6 questions from Domains 1 and 6, testing recall of AI/ML definitions, terms, and basic facts
K2 – Understand: Each question worth 1 point, ~15 questions from Domains 1, 2, 3, 5, 6, 7, 8, 10, 11, testing ability to explain concepts and interpret results
K3 – Apply: Each question worth 2 points, ~12 questions from Domains 3, 4, 5, 9, 11, testing practical application of AI testing methods, dataset prep, ML metrics, and tasks
K4 – Analyze: Each question worth 2 points, ~7 questions from Domains 8 and 9, focusing on analyzing AI test strategies, evaluating bias, and assessing explainability
Total: 40 questions for 47 points, balanced across foundational knowledge, applied skills, and analytical abilities.
Practice Test Structure & Preparation Strategy
Prepare for the ISTQB Certified Tester – AI Testing (CT-AI) certification exam with realistic, exam-style mock tests that build conceptual understanding, hands-on readiness, and exam confidence.
6 Full-Length Practice Tests: 6 complete mock exams with 40 questions each (240 Questions), timed and scored, reflecting the real exam structure, style, and complexity
Diverse Question Categories: Questions are designed across multiple cognitive levels (K1–K4)
Knowledge-Heavy Questions (K1–K2): Worth 1 point each, focus on recalling theory, definitions, and basic AI/ML concepts (~50% of questions)
Application & Analysis Questions (K3–K4): Scenario-based or analytical, worth 2 points each, testing application, reasoning, and analysis (~50% of total points)
Hands-On Elements (H1–H2): Practical activities from Domains 4–6, 8–9, 11 reinforce application/analysis, strengthen understanding of real-world AI testing tasks
Comprehensive Explanations: Detailed reasoning for correct and incorrect options to enhance learning
Timed & Scored Simulation: Practice under realistic exam timing to develop focus, pacing, and endurance
Randomized Question Bank: Questions and answer options reshuffle in each attempt to prevent memorization
Performance Analytics: Domain-wise insights to identify strengths and areas for improvement, focus on AI quality characteristics, ML workflows, bias detection, and explainability (XAI)
Question 1
A medical research team is developing a machine learning system to support clinical diagnosis of a specific disease based on patient laboratory test results, medical imaging findings, and historical patient records. The system must produce a categorical diagnostic decision (disease present or disease absent) along with confidence probability scores to inform physician decision-making and treatment planning. The output will directly influence clinical interventions and patient care pathways.
Which ONE of the following options BEST describes the appropriate machine learning approach for this diagnostic scenario?
Options:
A. Regression analysis to predict continuous disease progression scores and biomarker concentration levels.
B. Binary classification to predict disease presence or absence with associated probability estimates indicating model confidence.
C. Clustering analysis to identify natural patient subgroups based on similar symptom patterns and test result profiles.
D. Reinforcement learning to optimize sequential treatment protocol decisions based on patient response patterns.
Answer: B
Explanation:
A: Incorrect because regression is designed for continuous numeric outputs, not categorical yes/no decisions with confidence scores.
B: Cor
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