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ISQI CT-AI_v1.0_World

CT-AI_v1.0_World

Exam Code: CT-AI_v1.0_World

Exam Name: ISTQB Certified Tester AI Testing (v1.0)

Updated: Aug 15, 2026

Q&A Number: 40 Q&As

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ISQI CT-AI_v1.0_World Exam Syllabus Topics:

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

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

1. Which ONE of the following combinations of Training, Validation, Testing data is used during the process of learning/creating the model?
SELECT ONE OPTION

A) Training data * test data
B) Training data - validation data
C) Validation data - test data
D) Training data - validation data - test data


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


3. 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) Threshold coverage
B) Neuron coverage
C) Value coverage
D) Sign change coverage


4. Which ONE of the following options BEST DESCRIBES clustering?
SELECT ONE OPTION

A) Clustering is classification of a continuous quantity.
B) Clustering is supervised learning.
C) Clustering is done without prior knowledge of output classes.
D) Clustering requires you to know the classes.


Solutions:

Question # 1
Answer: D
Question # 2
Answer: B
Question # 3
Answer: D
Question # 4
Answer: C

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