ISQI CT-AI_v1.0_World Exam Information and Actual Questions

  • Exam Code/Number: CT-AI_v1.0_World
  • Exam Name/Title: ISTQB Certified Tester AI Testing (v1.0)
  • Certification Provider: ISQI
  • Corresponding Certification: AI Testing
  • Exam Questions: 40
  • Updated On: Jul 17, 2026

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

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ISQI CT-AI_v1.0_World Exam Overview:

Certification Vendor:iSQI / ISTQB
Exam Name:ISTQB Certified Tester AI Testing (v1.0)
Exam Number:CT-AI_v1.0_World
Exam Format:Multiple Choice
Certificate Validity Period:Lifetime
Available Languages:English, French, Portuguese, Other regional languages depending on availability
Related Certifications:ISTQB Certified Tester Foundation Level (CTFL)
Real Exam Qty:40
Passing Score:65% (31/47 points)
Exam Price:$250 USD
Exam Duration:60-75
Sample Questions:ISQI CT-AI_v1.0_World Sample Questions
Exam Way:Computer-based exam available through Pearson VUE test centers and iSQI FLEX remote proctoring.
Pre Condition:Candidates must hold a valid ISTQB Certified Tester Foundation Level (CTFL) certification.
Official Syllabus URL:https://isqi.org/ISTQB-Certified-Tester-AI-Testing-v1.0-CT-AI/CT-AI.157

ISQI CT-AI_v1.0_World Exam Syllabus Topics:

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


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