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

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:

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 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 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)

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)

Bias: algorithmic, sample, inappropriate

Ethics, side effects, reward hacking

Transparency, interpretability, explainability (XAI)

Safety in AI systems

ML workflow: training, evaluation, tuning, testing

Algorithm selection factors

Overfitting and underfitting

Training, validation, and test datasets

Data quality issues and their impact

Data labeling approaches and mislabeling causes

ROC curve, AUC, MSE, R-squared, silhouette coefficient

Limitations and selection of metrics

Benchmark suites (e.g., MLCommons)

Coverage measures: neuron, threshold, sign-change, value-change, sign-sign

Test levels: input data, model, component, integration, system, acceptance

Test data challenges, automation bias, concept drift

Documentation and test approach selection

Testing for bias, transparency, interpretability, explainability

Test oracles and acceptance criteria

Pairwise, back-to-back, A/B, metamorphic testing

Experience-based and exploratory testing

Test technique selection

Benefits of virtual test environments

Defect analysis, test case generation, regression optimization

Defect prediction

GUI testing with AI

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):

Worth 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 workflows

Analytical (K3–K4, H2):

Worth 12 points (~25% of the exam)

Evaluates ability to analyze AI test strategies, identify bias, and assess explainability (XAI) in AI systems

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

Diverse Question Categories: Questions are designed across multiple cognitive levels (K1–K4)

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)

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.

B: Correct. Binary classification algorithms (e.g., logistic regression, neural networks) provide categorical predictions (present/absent) with probability scores, directly meeting the requirement for a diagnostic decision with confidence.

C: Incorrect because clustering is an unsupervised technique for finding natural groupings; it does not predict a known diagnostic outcome or provide confidence scores.

D: Incorrect because reinforcement learning is suited for sequential decision-making over time, not for a single-point diagnostic prediction.

B. Non-deterministic decision-making due to probabilistic outputs.

C. Inability to provide complete formal verification of model behavior.

D. Unpredictable responses to inputs outside the training distribution.

B: Incorrect. Probabilistic outputs that lead to non-deterministic decisions introduce significant challenges for safety certification, hazard analysis, and guaranteeing consistent system performance.

C: Incorrect. The lack of formal, mathematical verification is a major barrier to proving the safety and correctness of AI systems, as required in domains like aviation or automotive.

D: Incorrect. Unpredictable failure when the system encounters scenarios not seen during training (out-of-distribution inputs) creates a serious safety risk and makes assurance extremely difficult.

B. I, III only

C. II, IV, V only

D. III only

B: Incorrect because it excludes computer vision, machine learning, and genetic algorithms, which are all valuable AI technologies for testing.

C: Incorrect because it excludes natural language processing, which is key for processing textual test artifacts like requirements and defect reports.

D: Incorrect because rule-based expert systems are just one type of classical AI. This option ignores the broader spectrum of applicable AI technologies.

Target 80%+ in Practice Tests: The real exam requires 31/47 points to pass; achieving higher scores in practice builds confidence and mastery

Review Explanations in Detail: Carefully study why each answer is correct or incorrect to avoid conceptual mistakes

Simulate Real Exam Conditions: Attempt mock tests in timed, distraction-free sessions to develop focus, speed, and exam endurance

Hands-On Application: Reinforce AI testing knowledge through practical examples like ML model validation, neural network testing, and bias analysis

Full syllabus coverage including AI fundamentals, ML workflows, bias detection, ethics, explainability, neural networks, and AI test automation

In-depth explanations for correct and incorrect answers to improve understanding

Timed, scored tests with randomized questions for better preparation

Designed for AI testers, QA engineers, and developers preparing for ISTQB CT-AI

Updated as per the latest ISTQB CT-AI syllabus.

100% coverage of official ISTQB CT-AI syllabus

Realistic multiple-choice and select-all-that-apply questions

Detailed rationales for correct and incorrect answers

Balanced question distribution across K1–K4 levels

Timed simulations to replicate exam conditions

Randomized question bank for active learning

Accessible anywhere, anytime on desktop or mobile

Lifetime updates included for syllabus changes

Access on Mobile: Study anytime, anywhere on your phone or tablet

Full Lifetime Access: Learn at your own pace with no expiration

QA engineers, test leads, and automation testers entering AI testing

Developers and IT professionals enhancing AI testing skills

AI/ML enthusiasts aiming for ISTQB AI Testing Certification

Professionals addressing real-world AI testing challenges like bias, transparency, and non-deterministic outputs

Career changers seeking expertise in AI QA and test automation

AI test design, execution, and validation techniques

Bias detection, explainability (XAI), ethics, and AI system safety

Scenario-based testing for AI/ML and neural networks

Using AI tools and automation frameworks for testing

Time management and exam strategies for ISTQB CT-AI

Practical knowledge to confidently pass the ISTQB CT-AI certification exam

Basic understanding of software testing principles

Familiarity with AI, ML, or neural network concepts is helpful but not required

Computer with internet access for online mock exams

Curiosity to learn AI testing, bias detection, and AI model validation techniques.