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: Aug 04, 2026   Q&As: 40 Questions and Answers

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

1. Which ONE of the following describes a situation of back-to-back testing the LEAST?
SELECT ONE OPTION

A) Comparison of the results of a neural network ML model with a current decision tree ML model for the same data.
B) Comparison of the results of a current neural network model ML model implemented in platform A (for example Pytorch) with a similar neural network model ML model implemented in platform B (for example Tensorflow), for the same data.
C) Comparison of the results of a home-grown neural network model ML model with results in a neural network model implemented in a standard implementation (for example Pytorch) for same data
D) Comparison of the results of the current neural network ML model on the current data set with a slightly modified data set.


2. ln the near future, technology will have evolved, and Al will be able to learn multiple tasks by itself without needing to be retrained, allowing it to operate even in new environments. The cognitive abilities of Al are similar to a child of 1-2 years.' In the above quote, which ONE of the following options is the correct name of this type of Al?
SELECT ONE OPTION

A) Super Al
B) General Al
C) Technological singularity
D) Narrow Al


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


4. Which ONE of the following options represents a technology MOST TYPICALLY used to implement Al?
SELECT ONE OPTION

A) Search engines
B) Case control structures
C) Genetic algorithms
D) Procedural programming


Solutions:

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

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