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ISQI CT-AI_v1.0_World : 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: Oct 06, 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. Monitoring and Maintenance
  • 2. Data Management
  • 3. Environment Configuration
Methods and Techniques for Testing AI-Based Systems- Test Design Techniques
  • 1. Data-Oriented Testing
  • 2. Scenario-Based Testing
  • 3. Model Testing
Introduction to AI- AI Fundamentals
  • 1. AI Development Frameworks
  • 2. AI-Based vs Conventional Systems
  • 3. AI Technologies
  • 4. AI as a Service
  • 5. AI Definitions and Concepts
Quality Characteristics for AI-Based Systems- AI-Specific Quality Attributes
  • 1. Transparency and Explainability
  • 2. Flexibility and Adaptability
  • 3. Autonomy
  • 4. Bias
  • 5. Ethics
  • 6. Evolution
ML Functional Performance Metrics- Performance Evaluation
  • 1. Precision
  • 2. Recall
  • 3. Accuracy
  • 4. F1 Score
Testing AI-Specific Quality Characteristics- Quality Validation
  • 1. Explainability Assessment
  • 2. Robustness Testing
  • 3. Fairness Testing
Machine Learning- ML Fundamentals
  • 1. Machine Learning Overview
  • 2. Supervised and Unsupervised Learning
  • 3. Training and Validation
ML Data- Data Quality
  • 1. Data Collection
  • 2. Data Preparation
  • 3. Data Bias
ML Neural Networks and Testing- Neural Networks
  • 1. Testing Neural Networks
  • 2. Neural Network Basics
Testing AI-Based Systems Overview- AI Testing Fundamentals
  • 1. AI Test Lifecycle
  • 2. Risk-Based Testing
  • 3. Test Strategy
Using AI for Testing- AI-Assisted Testing
  • 1. Test Data Generation
  • 2. Test Automation Support
  • 3. Defect Prediction

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

Question #1
"AllerEgo" is a product that uses sell-learning to predict the behavior of a pilot under combat situation for a variety of terrains and enemy aircraft formations. Post training the model was exposed to the real- world data and the model was found to bebehaving poorly. A lot of data quality tests had been performed on the data to bring it into a shape fit for training and testing.
Which ONE of the following options is least likely to describes the possible reason for the fall in the performance, especially when considering the self-learning nature of the Al system?
SELECT ONE OPTION

A. The difficulty of defining criteria for improvement before the model can be accepted.
B. The fast pace of change did not allow sufficient time for testing.
C. There was an algorithmic bias in the Al system.
D. The unknown nature and insufficient specification of the operating environment might have caused the poor performance.


Question #2
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. Check the input test data for potential sample bias.
D. Testing the data pipeline for any sources for algorithmic bias.


Question #3
Written requirements are given in text documents, which ONE of the following options is the BEST way to generate test cases from these requirements?
SELECT ONE OPTION

A. Analyzing source code for generating test cases
B. Natural language processing on textual requirements
C. Machine learning on logs of execution
D. GUI analysis by computer vision


Question #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 requires you to know the classes.
D. Clustering is done without prior knowledge of output classes.


Solutions:

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

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