ISQI ISTQB Certified Tester AI Testing (v1.0) : CT-AI_v1.0_World

  • Exam Code: CT-AI_v1.0_World
  • Exam Name: ISTQB Certified Tester AI Testing (v1.0)
  • Updated: Jul 21, 2026
  • Q & A: 40 Questions and Answers

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ISQI CT-AI_v1.0_World Exam Syllabus Topics:
SectionObjectives
Topic 1: Using AI for Testing- AI-Assisted Testing
  • 1. Test Data Generation
  • 2. Defect Prediction
  • 3. Test Automation Support
Topic 2: Testing AI-Specific Quality Characteristics- Quality Validation
  • 1. Fairness Testing
  • 2. Explainability Assessment
  • 3. Robustness Testing
Topic 3: ML Data- Data Quality
  • 1. Data Collection
  • 2. Data Bias
  • 3. Data Preparation
Topic 4: Introduction to AI- AI Fundamentals
  • 1. AI Technologies
  • 2. AI Development Frameworks
  • 3. AI as a Service
  • 4. AI Definitions and Concepts
  • 5. AI-Based vs Conventional Systems
Topic 5: ML Functional Performance Metrics- Performance Evaluation
  • 1. Precision
  • 2. F1 Score
  • 3. Recall
  • 4. Accuracy
Topic 6: Test Environments for AI-Based Systems- AI Test Infrastructure
  • 1. Environment Configuration
  • 2. Data Management
  • 3. Monitoring and Maintenance
Topic 7: Quality Characteristics for AI-Based Systems- AI-Specific Quality Attributes
  • 1. Evolution
  • 2. Ethics
  • 3. Autonomy
  • 4. Transparency and Explainability
  • 5. Flexibility and Adaptability
  • 6. Bias
Topic 8: Methods and Techniques for Testing AI-Based Systems- Test Design Techniques
  • 1. Model Testing
  • 2. Scenario-Based Testing
  • 3. Data-Oriented Testing
Topic 9: Machine Learning- ML Fundamentals
  • 1. Supervised and Unsupervised Learning
  • 2. Machine Learning Overview
  • 3. Training and Validation
Topic 10: ML Neural Networks and Testing- Neural Networks
  • 1. Testing Neural Networks
  • 2. Neural Network Basics
Topic 11: Testing AI-Based Systems Overview- AI Testing Fundamentals
  • 1. Test Strategy
  • 2. Risk-Based Testing
  • 3. AI Test Lifecycle
ISQI ISTQB Certified Tester AI Testing (v1.0) Sample Questions:

1. Which ONE of the following tests is MOST likely to describe a useful test to help detect different kinds of biases in ML pipeline?
SELECT ONE OPTION

A) Test the model during model evaluation for data bias.
B) Testing the distribution shift in the training data for inappropriate bias.
C) Testing the data pipeline for any sources for algorithmic bias.
D) Check the input test data for potential sample bias.


2. Which ONE of the following is the BEST option to optimize the regression test selection and prevent the regression suite from growing large?
SELECT ONE OPTION

A) Using an Al-based tool to optimize the regression test suite by analyzing past test results
B) Automating test scripts using Al-based test automation tools.
C) Identifying suitable tests by looking at the complexity of the test cases.
D) Using of a random subset of tests.


3. A software component uses machine learning to recognize the digits from a scan of handwritten numbers. In the scenario above, which type of Machine Learning (ML) is this an example of?
SELECT ONE OPTION

A) Classification
B) Clustering
C) Regression
D) Reinforcement learning


4. Which ONE of the following options describes a scenario of A/B testing the LEAST?
SELECT ONE OPTION

A) A comparison of the performance of two different ML implementations on the same input data.
B) A comparison of two different websites for the same company to observe from a user acceptance perspective.
C) A comparison of two different offers in a recommendation system to decide on the more effective offer for same users.
D) A comparison of the performance of an ML system on two different input datasets.


Solutions:

Question # 1
Answer: A
Question # 2
Answer: A
Question # 3
Answer: A
Question # 4
Answer: D

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