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

  • Exam Code: CT-AI_v1.0_World
  • Exam Name: ISTQB Certified Tester AI Testing (v1.0)
  • Updated: Oct 03, 2026
  • Q & A: 40 Questions and Answers

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ISQI CT-AI_v1.0_World Exam Syllabus Topics:

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

ISQI ISTQB Certified Tester AI Testing (v1.0) Sample Questions:

Question #1

Which ONE of the following options does NOT describe a challenge for acquiring test data in ML systems?
SELECT ONE OPTION

  • A. Data for the use case is being generated at a fast pace.
  • B. Nature of data constantly changes with lime.
  • C. Compliance needs require proper care to be taken of input personal data.
  • D. Test data being sourced from public sources.
Reveal Solution  Discussion  0

Correct Answer: A  🗳️

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Question #2

Data used for an object detection ML system was found to have been labelled incorrectly in many cases.
Which ONE of the following options is most likely the reason for this problem?
SELECT ONE OPTION

  • A. Bias issues
  • B. Accuracy issues
  • C. Security issues
  • D. Privacy issues
Reveal Solution  Discussion  0

Correct Answer: B  🗳️

Question #3

Which ONE of the following models BEST describes a way to model defect prediction by looking at the history of bugs in modules by using code quality metrics of modules of historical versions as input?
SELECT ONE OPTION

  • A. Search of similar code based on natural language processing.
  • B. Using a classification model to predict the presence of a defect by using code quality metrics as the input data.
  • C. Clustering of similar code modules to predict based on similarity.
  • D. Identifying the relationship between developers and the modules developed by them.
Reveal Solution  Discussion  0

Correct Answer: B  🗳️

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Question #4

Which ONE of the following types of coverage SHOULD be used if test cases need to cause each neuron to achieve both positive and negative activation values?
SELECT ONE OPTION

  • A. Neuron coverage
  • B. Sign change coverage
  • C. Value coverage
  • D. Threshold coverage
Reveal Solution  Discussion  0

Correct Answer: B  🗳️

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