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

Are you preparing for the ISTQB Certified Tester - AI Testing (CT-AI) 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-AI 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-AI 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-AI 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 regularly to ensure alignment with the latest ISTQB CT-AI syllabus.

This CT-AI practice test course includes:240 exam-style questions across 6 timed practice exams (40 questions each). Detailed explanations for both correct and incorrect options. Realistic exam simulation with scoring and timing.

Updated syllabus coverage aligned with ISTQB CT-AI v2026. Performance reports to identify strengths and weaknesses. Domain and K-Level mapping for every question (covering K1–K4 across all 8 syllabus domains).

Free coupon access to the complete practice exam for a limited time. With this course, you’ll not only practice but also master AI testing concepts such as AI fundamentals, testing AI-based systems, quality challenges in AI, and ethical considerations in AI testing. Exam Details – ISTQB CT-AI Certification (with K-level breakdown)Exam Body: ISTQB (International Software Testing Qualifications Board)Certification Name: ISTQB Certified Tester – AI Testing (CT-AI)Format: Multiple Choice Questions (MCQs)Number of Questions: 40Duration: 60 minutes (75 minutes for non-native English speakers)Passing Score: 65% (26 out of 40 correct)Difficulty Level: Foundation to IntermediateLanguage: English (localized versions may be available)Certification Validity: Lifetime (no renewal required)Exam Mode: Online proctored or at authorized test centersK-Level Distribution (overall)The exam targets Bloom’s-based K-levels.

Suggested distribution aligned to the CT-AI syllabus:K1 (Remember / Define / List): 13 questionsK2 (Understand / Explain / Compare): 22 questionsK3 (Apply / Use / Execute): 3 questionsK4 (Analyze / Evaluate / Select): 2 questions Overall emphasis: K1–K2, with limited coverage of K3 and K4, matching a foundation-to-intermediate exam. Per-Chapter Question & K-Level Allocation (matches your syllabus)(Useful for mapping practice questions to chapters on Udemy)Chapter 1 — Introduction to AI — 4 questions → K1: 1, K2: 3Chapter 2 — Quality Characteristics — 4 questions → K1: 1, K2: 3Chapter 3 — ML Overview — 4 questions → K2: 3, K3: 1Chapter 4 — ML — Data — 4 questions → K1: 1, K2: 3Chapter 5 — ML Functional Metrics — 3 questions → K2: 1, K3: 1, K4: 1Chapter 6 — Neural Networks & Testing — 2 questions → K2: 2Chapter 7 — Testing AI Systems Overview — 4 questions → K1: 1, K2: 2, K4: 1Chapter 8 — AI-Specific Quality Testing — 4 question → K2: 3, K4: 1Chapter 9— Methods and Techniques for the Testing of AI-Based Systems — 6 question → K2: 4, K3: 1, K4: 1Chapter 10 — Test Environments for AI-Based Systems — 1 question → K2: 1Chapter 11 — Using AI for Testing — 4 question → K2: 4Detailed Syllabus and Topic Weightage:The ISTQB CT-AI exam is structured around several major syllabus areas. Below is a detailed breakdown along with the approximate number of questions you can expect from each topic:1.

Chapter 1: Introduction to AI (4 Questions | K1–K2)Understand AI definitions, types (Narrow, General, Super AI), and real-world impactCompare AI-based and traditional systemsExplore AI technologies, development frameworks, and AI hardwareLearn about AI as a Service (AIaaS), pre-trained models, and AI standardsK-Level Focus: Concept understanding and differentiation (K1–K2)2. Chapter 2: Quality Characteristics for AI-Based Systems (4 Questions | K1–K2)Understand flexibility, adaptability, autonomy, and evolution in AIAddress ethics, bias, and reward hackingExplore transparency, interpretability, explainability, and AI safetyK-Level Focus: Explain and recognize key AI quality attributes (K1–K2)3. Chapter 3: Machine Learning (ML) Overview (4 Questions | K2–K3)Learn ML types (supervised, unsupervised, reinforcement)Follow the ML workflow and selection guidelinesUnderstand overfitting, underfitting, and performance trade-offs K-Level Focus: Comprehend and apply ML principles (K2–K3)4.

Chapter 4: ML – Data (4 Questions | K1–K2)Data preparation, labelling, feature engineering, and dataset splittingHandle data quality issues (wrong, incomplete, biased data)Understand how poor data impacts ML modelsK-Level Focus: Recognize, describe, and interpret data concepts (K1–K2)5. Chapter 5: ML Functional Performance Metrics (5 Questions | K2–K4)Learn confusion matrix, accuracy, precision, recall, F1-scoreExplore ROC, AUC, MSE, and clustering metricsChoose metrics based on test goals and data types K-Level Focus: Analyze and evaluate ML metrics (K2–K4)6. Chapter 6: Neural Networks and Testing (2 Questions | K2)Understand neural network architecture and key termsLearn about neural coverage measuresK-Level Focus: Explain and interpret NN testing concepts (K2)7.

Chapter 7: Testing AI-Based Systems Overview (4 Questions | K1–K2, K4)Understand test levels, test data, automation bias, and concept driftLearn about documentation (Factsheets, Model Cards)Choose the right testing approach for AI systemsK-Level Focus: Explain, apply, and evaluate testing concepts (K1-K2, K4)8. Chapter 8: Testing AI-Specific Quality Characteristics (4 Questions | K2, K4)Testing bias, probabilistic behavior, explainability, and complexityDefine test objectives and acceptance criteria for AIK-Level Focus: Explain and analyze AI-specific test challenges (K2)9. Chapter 9: Testing Techniques (6 Questions | K2, K3, K4)10.

Chapter 10: Test Environments (1 Question | K2)11. Chapter 11: Using AI for Testing (4 Questions | K2)Practice Test Structure:6 Full-Length TestsEach test contains 40 exam-style questionsIncludes questions from all CT-AI syllabus domainsDetailed Feedback and ExplanationsEvery question includes a one-liner explanation for correct and incorrect answersHelps reinforce learning and prevent repeated mistakesRandomized OrderEach time you attempt a test, questions and answer choices are randomized. Prevents memorization and ensures real exam readinessProgress TrackingAfter completing a test, you will see your score, pass/fail status, and areas that need focusSample Practice Questions:Question 1:Which of the following BEST describes the primary difference between supervised and unsupervised learning?

Options:A. Supervised learning requires human intervention during training while unsupervised learning is fully automatedB. Supervised learning uses labeled data to learn patterns while unsupervised learning discovers patterns in unlabeled dataC.

Supervised learning is used for classification tasks while unsupervised learning is used for regression tasksD. Supervised learning produces more accurate results than unsupervised learning in all scenariosAnswer: C. Supervised learning is used for classification tasks while unsupervised learning is used for regression tasksExplanation of each option:A.

Supervised learning requires labeled training data provided by humans but the training process itself is automated through algorithms. Unsupervised learning also uses automated training processes. The key distinction lies in whether the training data includes labeled examples not in the level of automation during training.

Both forms of ML use automated learning algorithms once the data is prepared. B. Supervised learning requires labeled training data where each input has a known correct output allowing the algorithm to learn the mapping between inputs and outputs.

Unsupervised learning works with unlabeled data discovering hidden patterns structures or groupings without predefined categories. This fundamental difference in data requirements determines which form of ML is appropriate for different problem types as defined in ISTQB CT-AI Syllabus Chapter 3. C.

This incorrectly characterizes the relationship between ML forms and task types. Supervised learning includes both classification and regression tasks while unsupervised learning includes clustering and association tasks. The distinction between supervised and unsupervised learning is based on whether labeled training data is available not on the specific task type being performed.

D. Accuracy depends on the problem context data quality and appropriateness of the ML approach not inherently on whether the learning is supervised or unsupervised. Unsupervised learning can be highly effective for discovering patterns in unlabeled data where supervised learning would be impractical.

The choice between forms of ML should be based on the problem requirements and available data not assumed accuracy levels. Chapter and K-Level: Chapter 3: Machine Learning - Overview - K2Question 2:An aerospace company is developing an AI-based flight control system. The test manager needs to plan testing activities across different abstraction levels to ensure comprehensive quality assurance.

At which test level should the integration between the AI component and the aircraft's sensor systems be validated? Options:A. ML model testingB.

Acceptance testingC. Component integration testingD. Input data testingAnswer: C.

Component integration testingExplanation of each option:A. ML model testing focuses on validating the ML model's functional performance using metrics like accuracy precision and recall on test datasets. ISTQB describes ML model testing as assessing whether the model meets functional requirements before integration.

This level tests the model in isolation not its integration with other system components. Sensor integration occurs at a higher abstraction level after model validation. B.

Acceptance testing validates that the complete system meets business requirements and user needs in operational conditions. ISTQB positions acceptance testing as the final validation before deployment typically performed by end users or customers. While acceptance testing includes integrated system behavior it focuses on overall system acceptance not specifically on component integration.

Integration testing precedes acceptance testing in the test level hierarchy. C. Component integration testing validates the interactions and interfaces between integrated components such as the AI component and sensor systems.

ISTQB describes component integration testing as verifying that components work correctly together through their interfaces. In the flight control system this level ensures the AI component correctly receives and processes sensor data validates data exchange protocols and confirms proper error handling. Testing sensor integration at this level identifies interface defects before system-level testing making it the appropriate test level for validating AI and sensor system integration.

D. Input data testing focuses on validating the quality characteristics and suitability of training validation and test datasets not component integration.