ISTQB AI Testing (CT-AI) Mock Tests - 240 Questions - 2026

About this course

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-GenAI practice exams 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 CoverageThis 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 Unique6 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 DetailsExam Body: ISTQB (International Software Testing Qualifications Board)Exam Name: ISTQB Certified Tester – AI Testing (CT-AI)Prerequisite Certification: ISTQB Certified Tester Foundation Level (CTFL)Exam Format: Multiple Choice Questions (MCQs) – single and multiple-select questionsCertification Validity: Lifetime (no renewal required)Number of Questions: 40Total Points: 47 pointsPassing 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 WeightageThe 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 AIAI vs. conventional systemsAI technologies, frameworks, and hardwareAI as a Service (AlaaS), pre-trained models, transfer learningStandards and regulations (e.g., GDPR, ISO)Domain 2: Quality Characteristics for AI-Based Systems (~10–12%)Flexibility, adaptability, autonomy, evolutionBias: algorithmic, sample, inappropriateEthics, side effects, reward hackingTransparency, interpretability, explainability (XAI)Safety in AI systemsDomain 3: Machine Learning (ML) Overview (~8–10%)Supervised, unsupervised, reinforcement learningML workflow: training, evaluation, tuning, testingAlgorithm selection factorsOverfitting and underfittingDomain 4: ML Data (~8–10%)Data preparation: acquisition, preprocessing, feature engineeringTraining, validation, and test datasetsData quality issues and their impactData labeling approaches and mislabeling causesDomain 5: ML Functional Performance Metrics (~6–8%)Confusion matrix, accuracy, precision, recall, F1-scoreROC curve, AUC, MSE, R-squared, silhouette coefficientLimitations and selection of metricsBenchmark suites (e.g., MLCommons)Domain 6: ML Neural Networks and Testing (~6–8%)Structure and function of neural networks and DNNsCoverage measures: neuron, threshold, sign-change, value-change, sign-signDomain 7: Testing AI-Based Systems Overview (~10–12%)Specification challengesTest levels: input data, model, component, integration, system, acceptanceTest data challenges, automation bias, concept driftDocumentation and test approach selectionDomain 8: Testing AI-Specific Quality Characteristics (~8–10%)Self-learning, autonomous, probabilistic, complex systemsTesting for bias, transparency, interpretability, explainabilityTest oracles and acceptance criteriaDomain 9: Methods and Techniques for Testing AI-Based Systems (~10–12%)Adversarial attacks, data poisoningPairwise, back-to-back, A/B, metamorphic testingExperience-based and exploratory testingTest technique selectionDomain 10: Test Environments for AI-Based Systems (~4–6%)Unique test environment needsBenefits of virtual test environmentsDomain 11: Using AI for Testing (~4–6%)AI technologies in testingDefect analysis, test case generation, regression optimizationDefect predictionGUI testing with AIISTQB CT-AI Exam Categories and WeightageThe 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 10Worth 12 points (~26% of the exam)Focuses on basic AI concepts, quality characteristics, testing fundamentals, and recalling key definitionsApplied (K2–K3, H1–H2):Domains 3, 4, 5, and 11Worth 23 points (~49% of the exam)Tests ability to apply knowledge in practical scenarios, including data preparation, ML metrics, AI testing methods, and using AI in testing workflowsAnalytical (K3–K4, H2):Domains 8 and 9Worth 12 points (~25% of the exam)Evaluates ability to analyze AI test strategies, identify bias, and assess explainability (XAI) in AI systemsISTQB CT-AI Exam K-Level DistributionK1 – Remember: Each question is worth 1 point, ~6 questions from Domains 1 and 6, testing recall of AI/ML definitions, terms, and basic factsK2 – 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 resultsK3 – 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 tasksK4 – Analyze: Each question worth 2 points, ~7 questions from Domains 8 and 9, focusing on analyzing AI test strategies, evaluating bias, and assessing explainabilityTotal: 40 questions for 47 points, balanced across foundational knowledge, applied skills, and analytical abilities.Practice Test Structure & Preparation StrategyPrepare 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 complexityDiverse 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 tasksComprehensive Explanations: Detailed reasoning for correct and incorrect options to enhance learningTimed & Scored Simulation: Practice under realistic exam timing to develop focus, pacing, and enduranceRandomized Question Bank: Questions and answer options reshuffle in each attempt to prevent memorizationPerformance Analytics: Domain-wise insights to identify strengths and areas for improvement, focus on AI quality characteristics, ML workflows, bias detection, and explainability (XAI)Sample Practice QuestionsQuestion 1Your organization uses AI-driven regression test optimization to select a subset of tests from a comprehensive test suite for each code change. How should you validate the effectiveness of this optimization approach?Options:A. Measure only the reduction in test execution time and assume quality is maintained.B. Monitor defect escape rates, compare against baseline testing approaches, and validate that the optimized subset catches the same proportion of regressions as full test suite execution.C. Replace the comprehensive test suite entirely with only the optimized subset to reduce testing costs.D. Assume the AI optimization is always correct without ongoing measurement and validation.Answer: BExplanation:A: Test execution time alone does not validate quality; defect escape rates must be measured.B: Validation requires empirical data on defect detection effectiveness; optimization should not increase defect escape rates.C: Eliminating the baseline test suite prevents validation and creates blind spots in regression coverage.D: Continuous validation is necessary to ensure optimization maintains quality standards.Domain: Using AI for Testing – Regression Test Optimization with AIK-Level: K2 – UnderstandQuestion 2Which framework is specifically designed for building and training deep learning models with automatic differentiation capabilities?Options:A. Scikit-learnB. TensorFlowC. Apache SparkD. HadoopAnswer: BExplanation:A: Scikit-learn focuses on traditional machine learning algorithms and does not provide deep learning infrastructure or automatic differentiation.B: TensorFlow is an open-source framework developed by Google for building neural networks, with automatic differentiation through its computational graph approach. It supports deployment across multiple platforms and is widely used in industry.C: Apache Spark is for distributed computing and big data analytics. MLlib exists for machine learning but it is not designed specifically for deep learning or automatic differentiation.D: Hadoop is a distributed storage and processing framework for big data, without tools for deep learning model training or automatic differentiation.Domain: Introduction to AI – AI Technologies and FrameworksK-Level: K1 – RememberQuestion 3An AI testing team evaluates a recommendation system and finds that the model produces errors that vary by user behavior and content genres. Some user segments receive less accurate recommendations. Root cause analysis shows incomplete training data for certain genres, imbalanced representation of user preference types, and model weights that underweight certain populations. Which approach best addresses these quality issues?Options:A. Retrain the model with synthetic data to balance all user types equally without examining actual data distribution patterns.B. Increase overall model complexity through deeper neural networks to improve prediction accuracy for all segments uniformly.C. Conduct stratified testing across user segments and genres, identify performance gaps and root causes in data representation and model weights, then implement targeted data collection and retraining while validating improvements for all segments.D. Remove the underperforming user segments to improve overall average recommendation accuracy.Answer: CExplanation:A: Synthetic balancing without analyzing real patterns may introduce artificial relationships that don’t reflect real user behavior.B: Higher complexity does not solve root causes and may amplify existing biases without targeted data adjustments.C: Comprehensive root cause analysis with targeted improvements ensures quality issues are addressed while preventing new disparities.D: This violates fairness principles and is discriminatory; all users deserve equitable service.Domain: Testing AI-Specific Quality Characteristics – Complex Quality Issues and Root Cause AnalysisK-Level: K4 – AnalyzePreparation Strategy & Study GuidanceUnderstand the Concepts, Not Just the Questions: Use these tests to identify weak areas, but supplement study with official ISTQB CT-AI syllabus materialsTarget 80%+ in Practice Tests: The real exam requires 31/47 points to pass; achieving higher scores in practice builds confidence and masteryReview Explanations in Detail: Carefully study why each answer is correct or incorrect to avoid conceptual mistakesSimulate Real Exam Conditions: Attempt mock tests in timed, distraction-free sessions to develop focus, speed, and exam enduranceHands-On Application: Reinforce AI testing knowledge through practical examples like ML model validation, neural network testing, and bias analysisWhy This Course Is ValuableRealistic exam simulation aligned with ISTQB CT-AI format including knowledge levels (K1 to K4)Full syllabus coverage including AI fundamentals, ML workflows, bias detection, ethics, explainability, neural networks, and AI test automationIn-depth explanations for correct and incorrect answers to improve understandingTimed, scored tests with randomized questions for better preparationDesigned for AI testers, QA engineers, and developers preparing for ISTQB CT-AIUpdated as per the latest ISTQB CT-AI syllabus.Top Reasons to Take This Practice Exam6 full-length mock exams with 240+ questions100% coverage of official ISTQB CT-AI syllabusRealistic multiple-choice and select-all-that-apply questionsDetailed rationales for correct and incorrect answersBalanced question distribution across K1–K4 levelsTimed simulations to replicate exam conditionsRandomized question bank for active learningAccessible anywhere, anytime on desktop or mobileLifetime updates included for syllabus changesWhat This Course Includes6 Full-Length Practice Tests: Simulate real exam conditions to test your readinessAccess on Mobile: Study anytime, anywhere on your phone or tabletFull Lifetime Access: Learn at your own pace with no expirationMoney-Back GuaranteeYour success is our priority. If this course doesn’t meet your expectations, you’re covered by a 30-day no-questions-asked refund policy.Who This Course Is ForProfessionals preparing for the ISTQB CT-AI examQA engineers, test leads, and automation testers entering AI testingDevelopers and IT professionals enhancing AI testing skillsAI/ML enthusiasts aiming for ISTQB AI Testing CertificationProfessionals addressing real-world AI testing challenges like bias, transparency, and non-deterministic outputsCareer changers seeking expertise in AI QA and test automationWhat You’ll LearnCore AI and ML principles, including neural networks and ML workflowsAI test design, execution, and validation techniquesBias detection, explainability (XAI), ethics, and AI system safetyScenario-based testing for AI/ML and neural networksUsing AI tools and automation frameworks for testingTime management and exam strategies for ISTQB CT-AIPractical knowledge to confidently pass the ISTQB CT-AI certification examRequirements / PrerequisitesPrior ISTQB Foundation level (CTFL) certification requiredBasic understanding of software testing principlesFamiliarity with AI, ML, or neural network concepts is helpful but not requiredComputer with internet access for online mock examsCuriosity to learn AI testing, bias detection, and AI model validation techniques.

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