[100% Off] Istqb Ai Testing (Ct-Ai) Mock Tests - 240 Questions - 2025

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

Added on November 6, 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.
Address challenges like data bias
ethical considerations
and non-determinism in AI systems.
Prepare effectively for the ISTQB CT-AI certification exam with real-world scenarios and practice questions.
Learn how to design
execute
and validate test cases for AI
ML
and neural network systems effectively.
Explore AI model behavior
bias
and accuracy testing with real-world automation examples.
Gain confidence to tackle AI-specific testing challenges like dynamic outputs and non-deterministic behavior
Build skills in data validation
model drift detection
and explainability testing for AI systems.
Prepare strategically for the ISTQB CT-AI certification exam with full syllabus coverage
Practice with real-world AI testing scenarios and mock questions for hands-on learning.
Learn to use AI testing tools and frameworks for automation
validation
and performance checks.
Boost your QA career with in-demand AI testing skills and a recognized ISTQB CT-AI certification advantage.
Develop test strategies for AI lifecycle stages — from data prep to model deployment and monitoring.

Requirements

A basic understanding of software testing principles.
Familiarity with AI and machine learning concepts is beneficial but not mandatory.
Access to a computer with internet connectivity for engaging in hands-on practice and interactive learning.
Basic knowledge of software testing principles or QA fundamentals is recommended.
Familiarity with AI
machine learning
or neural networks is helpful but not required.
No programming expertise needed — all concepts explained clearly with real-world examples.
Access to a computer with internet connection for hands-on AI testing and practice exams.
Curiosity to learn AI testing
automation
and model validation techniques effectively.
Ideal for QA engineers
test leads
and automation testers expanding into AI testing.

Description

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 v2025.
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: 40
Duration: 60 minutes (75 minutes for non-native English speakers)
Passing Score: 65% (26 out of 40 correct)
Difficulty Level: Foundation to Intermediate
Language: English (localized versions may be available)
Certification Validity: Lifetime (no renewal required)
Exam Mode: Online proctored or at authorized test centers

K-Level Distribution (overall)

The exam targets Bloom’s-based K-levels. Suggested distribution aligned to the CT-AI syllabus:

K1 (Remember / Define / List): 13 questions
K2 (Understand / Explain / Compare): 22 questions
K3 (Apply / Use / Execute): 3 questions
K4 (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 — 12 questions → K1: 6, K2: 6
Chapter 2 — Quality Characteristics — 5 questions → K1: 2, K2: 3
Chapter 3 — ML Overview — 6 questions → K2: 4, K3: 2
Chapter 4 — ML — Data — 7 questions → K1: 4, K2: 3
Chapter 5 — ML Functional Metrics — 4 questions → K2: 2, K3: 1, K4: 1
Chapter 6 — Neural Networks & Testing — 2 questions → K2: 2
Chapter 7 — Testing AI Systems Overview — 3 questions → K1: 1, K2: 1, K4: 1
Chapter 8 — AI-Specific Quality Testing — 1 question → K2: 1

(Totals: 40 questions → K1:13, K2:22, K3:3, K4:2)

Detailed 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 (105 mins | 12 Questions | K1–K2)

Understand AI definitions, types (Narrow, General, Super AI), and real-world impact
Compare AI-based and traditional systems
Explore AI technologies, development frameworks, and AI hardware
Learn about AI as a Service (AIaaS), pre-trained models, and AI standards
K-Level Focus: Concept understanding and differentiation (K1–K2)

2. Chapter 2: Quality Characteristics for AI-Based Systems (105 mins | 5 Questions | K1–K2)

Understand flexibility, adaptability, autonomy, and evolution in AI
Address ethics, bias, and reward hacking
Explore transparency, interpretability, explainability, and AI safety
K-Level Focus: Explain and recognize key AI quality attributes (K1–K2)

3. Chapter 3: Machine Learning (ML) Overview (145 mins | 6 Questions | K2–K3)

Learn ML types (supervised, unsupervised, reinforcement)
Follow the ML workflow and selection guidelines
Understand overfitting, underfitting, and performance trade-offs
K-Level Focus: Comprehend and apply ML principles (K2–K3)

4. Chapter 4: ML – Data (230 mins | 7 Questions | K1–K2)

Data preparation, labelling, feature engineering, and dataset splitting
Handle data quality issues (wrong, incomplete, biased data)
Understand how poor data impacts ML models
K-Level Focus: Recognize, describe, and interpret data concepts (K1–K2)

5. Chapter 5: ML Functional Performance Metrics (120 mins | 4 Questions | K2–K4)

Learn confusion matrix, accuracy, precision, recall, F1-score
Explore ROC, AUC, MSE, and clustering metrics
Choose 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 (65 mins | 2 Questions | K2)

Understand neural network architecture and key terms
Learn about neural coverage measures
K-Level Focus: Explain and interpret NN testing concepts (K2)

7. Chapter 7: Testing AI-Based Systems Overview (115 mins | 3 Questions | K1–K4)

Understand test levels, test data, automation bias, and concept drift
Learn about documentation (Factsheets, Model Cards)
Choose the right testing approach for AI systems
K-Level Focus: Explain, apply, and evaluate testing concepts (K1–K4)

8. Chapter 8: Testing AI-Specific Quality Characteristics (150 mins | 1 Question | K2)

Testing bias, probabilistic behavior, explainability, and complexity
Define test objectives and acceptance criteria for AI
K-Level Focus: Explain and analyze AI-specific test challenges (K2)

9. Additional Reference Chapters (Not Included in Practice Exam)

Chapter 9: Testing Techniques (245 mins)
Chapter 10: Test Environments (30 mins)
Chapter 11: Using AI for Testing (195 mins)

Practice Test Structure:

6 Full-Length Tests
- Each test contains 40 exam-style questions
- Includes questions from all CT-AI syllabus domains
Detailed Feedback and Explanations
- Every question includes a one-liner explanation for correct and incorrect answers
- Helps reinforce learning and prevent repeated mistakes
Randomized Order
- Each time you attempt a test, questions and answer choices are randomized.
- Prevents memorization and ensures real exam readiness
Progress Tracking
- After completing a test, you will see your score, pass/fail status, and areas that need focus

Sample 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 automated
B. Supervised learning uses labeled data to learn patterns while unsupervised learning discovers patterns in unlabeled data
C. Supervised learning is used for classification tasks while unsupervised learning is used for regression tasks
D. Supervised learning produces more accurate results than unsupervised learning in all scenarios

Answer: C. Supervised learning is used for classification tasks while unsupervised learning is used for regression tasks

Explanation 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 – K2

Question 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 testing
B. Acceptance testing
C. Component integration testing
D. Input data testing

Answer: C. Component integration testing

Explanation 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. ISTQB describes input data testing as assessing data quality completeness and representativeness before ML model training. While sensor data quality is important input data testing occurs earlier in the ML workflow and does not address integration between AI components and sensor systems.

Chapter and K-Level: Chapter 7: Testing AI-Based Systems Overview – K2

Preparation Strategy & Guidance:

Understand the Exam Blueprint: Study the official ISTQB CT-AI syllabus thoroughly and focus on high-weightage topics.
Practice Under Exam Conditions: Use the 6 practice tests to simulate exam timing and environment.
Review Mistakes Carefully: Analyse incorrect answers to understand knowledge gaps.
Focus on Non-Deterministic Behavior: Spend extra time on AI testing challenges like bias, explainability, and data-driven testing.
Target 80%+ in Practice Exams: While 65% is the pass mark, consistently scoring above 80% in practice will ensure success in the real exam.
Continuous Revision: Reattempt practice tests until you are confident across all syllabus areas.

Why This Course is Valuable:

Real Exam Simulation: Each practice test is timed and scored to mirror the real CT-AI exam environment.
In-Depth Explanations: Every answer option (both correct and incorrect) is explained in one-liner clarity, building conceptual depth.
Coverage of Entire Syllabus: The 240 questions span across all CT-AI exam domains, ensuring full coverage.
Regular Updates: Updated consistently based on exam feedback and syllabus changes.
Skill Reinforcement: Helps test-takers internalise AI testing concepts, not just memorise them.
Confidence Building: By the end of the course, learners will feel fully prepared for test day.

Top Reasons Why These Practice Exams are the Key to Acing Your CT-AI Exam:

6 Complete Sets of Practice Exams: Covering 240 original, high-quality questions
100% Aligned with the CT-AI Syllabus:Structured to reflect real exam difficulty and topics
Simulates Actual Certification Exam: Time-limited, scored exams just like the real ISTQB CT-AI exam
Detailed Explanations: Every option is explained for maximum learning impact
Regularly Updated: Always aligned with the latest syllabus changes and exam patterns
Premium-Quality Content: Free of errors and written by experts in AI testing
Randomized Question Bank: Ensures genuine preparation instead of memorization
Best Value for Money: Lifetime access to all practice exams and updates
Mobile Access: Study anytime, anywhere
Track Your Progress: Get test reports to focus your preparation on weak areas.

Money-Back Guarantee:

This course comes with a 30-day unconditional money-back guarantee. If you feel that the practice tests do not meet your expectations or help you prepare for the ISTQB CT-AI exam, you can request a full refund—no questions asked.

Who This Course is For:

Testers preparing for the ISTQB Certified Tester – AI (CT-AI) certification exam
QA professionals looking to expand into AI and machine learning system testing
Software testers aiming to validate their AI knowledge with a globally recognized certification
Students and professionals who want to practice exam-style questions and measure their readiness
Test managers and leads who want to understand AI testing concepts to guide their teams
Anyone seeking to improve their AI testing career prospects with a prestigious certification.