[100% Off] Istqb - Certified Tester Ai Testing (Ct-Ai) - Practice Exams

Preparation for your ISTQB exam certification: Certified Tester AI Testing (CT-AI) | 6 Full Practice Exams Tests – 2025!

Added on November 7, 2025 IT & Software 10 min read

What you’ll learn

Understand AI testing principles and how they differ from traditional software testing.
Learn to design and execute test cases for AI-based systems
including neural networks and machine learning models.
Prepare effectively for the ISTQB CT-AI certification exam with real-world scenarios and practice questions
Boost your QA career with in-demand AI testing skills and a recognized ISTQB CT-AI certification advantage
Software testers and QA professionals preparing for ISTQB CT-AI certification
AI testers and AI QA engineers looking for real exam-like practice
Test automation engineers expanding into AI test automation and machine learning testing
Test automation engineers expanding into AI test automation and machine learning testing
Career changers seeking certification as AI Testers or AI QA Engineers
Anyone preparing for ISTQB AI Testing certification with hands-on mock exams
Professionals wanting to master scenario-based AI testing
Learners aiming to practice exam duration and time management skills
Testers wanting to improve knowledge of neural networks
XAI
bias detection
ethics
and AI test automation
Students preparing for K1-K4 level questions across the entire ISTQB CT-AI syllabus
QA leads seeking a realistic assessment tool to train teams for AI testing
Professionals looking for practice exams that simulate real ISTQB CT-AI conditions

Requirements

Basic understanding of software testing principles or ISTQB CTFL concepts is recommended
Familiarity with AI
machine learning
or neural networks is helpful but not mandatory
Access to a computer with internet connectivity to attempt hands-on ISTQB CT-AI mock exams
Curiosity to practice real-world AI testing scenarios and learn exam strategies
Willingness to analyze multiple-choice and select-all questions aligned with ISTQB CT-AI
No programming experience required — concepts like bias detection
XAI
AI test automation are explained in practice context
Interest in machine learning testing
AI model validation
and neural network testing
Ability to follow timed exam durations and simulate real ISTQB CT-AI conditions
Ideal for QA professionals
AI testers
automation engineers
and developers preparing for ISTQB AI Testing certification
Motivation to strengthen knowledge of K1-K4 syllabus topics
real-world scenario-based questions
and exam weightage distribution

Description

Course Overview

Prepare for the ISTQB® Certified Tester – AI Testing (CT-AI) Practice Exam 2025 with realistic, timed mock exams designed to replicate actual exam conditions.

Master AI testing, machine learning testing, and testing of AI-based systems while covering all K1–K4 syllabus topics. Gain confidence with 6 full-length mock exams featuring 240+ scenario-based questions, fully aligned with the latest official ISTQB® CT-AI syllabus, exam pattern, and topic-wise question distribution.

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 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)

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 practical scenarios, 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)

Sample Practice Questions

Question 1

Your 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: B

Explanation:

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 AI
K-Level: K2 – Understand

Question 2

Which framework is specifically designed for building and training deep learning models with automatic differentiation capabilities?

Options:

A. Scikit-learn
B. TensorFlow
C. Apache Spark
D. Hadoop

Answer: B

Explanation:

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 Frameworks
K-Level: K1 – Remember

Question 3

An 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: C

Explanation:

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 Analysis
K-Level: K4 – Analyze

Preparation Strategy & Study Guidance

Understand the Concepts, Not Just the Questions: Use these tests to identify weak areas, but supplement study with official ISTQB CT-AI syllabus materials
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

Why This Course Is Valuable

Realistic 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 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.

Top Reasons to Take This Practice Exam

6 full-length mock exams with 240+ questions
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

What This Course Includes

6 Full-Length Practice Tests: Simulate real exam conditions to test your readiness
Access on Mobile: Study anytime, anywhere on your phone or tablet
Full Lifetime Access: Learn at your own pace with no expiration

Money-Back Guarantee

Your 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 For

Professionals preparing for the ISTQB CT-AI exam
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

What You’ll Learn

Core AI and ML principles, including neural networks and ML workflows
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

Requirements / Prerequisites

Prior ISTQB Foundation level (CTFL) certification required
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.