ISQI CT-AI_v1.0_World Exam Questions : 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 27, 2026
  • Q&As: 40 Questions and Answers

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ISQI CT-AI_v1.0_World Exam Syllabus Topics:

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

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

Question #1

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

  • A. Genetic algorithms
  • B. Search engines
  • C. Procedural programming
  • D. Case control structures
Reveal Solution  Discussion  0

Correct Answer: A  🗳️

Explanation: Only visible for VCEPrep members. You can sign-up / login (it's free).

Question #2

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 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.
  • B. 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
  • C. Comparison of the results of a neural network ML model with a current decision tree ML model for the 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.
Reveal Solution  Discussion  0

Correct Answer: C  🗳️

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Question #3

"BioSearch" is creating an Al model used for predicting cancer occurrence via examining X-Ray images. The accuracy of the model in isolation has been found to be good. However, the users of the model started complaining of the poor quality of results, especially inability to detect real cancer cases, when put to practice in the diagnosis lab, leading to stopping of the usage of the model.
A testing expert was called in to find the deficiencies in the test planning which led to the above scenario.
Which ONE of the following options would you expect to MOST likely be the reason to be discovered by the test expert?
SELECT ONE OPTION

  • A. A lack of focus on choosing the right functional-performance metrics.
  • B. A lack of focus on non-functional requirements testing.
  • C. A lack of similarity between the training and testing data.
  • D. The input data has not been tested for quality prior to use for testing.
Reveal Solution  Discussion  0

Correct Answer: C  🗳️

Explanation: Only visible for VCEPrep members. You can sign-up / login (it's free).

Question #4

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

  • A. Clustering requires you to know the classes.
  • B. Clustering is classification of a continuous quantity.
  • C. Clustering is supervised learning.
  • D. Clustering is done without prior knowledge of output classes.
Reveal Solution  Discussion  0

Correct Answer: D  🗳️

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