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

CT-AI_v1.0_World pass collection

Exam Code: CT-AI_v1.0_World

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

Updated: Aug 23, 2026

Q & A: 40 Questions and Answers

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ISQI CT-AI_v1.0_World Exam Syllabus Topics:

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

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

1. A system was developed for screening the X-rays of patients for potential malignancy detection (skin cancer).
A workflow system has been developed to screen multiple cancers by using several individually trained ML models chained together in the workflow.
Testing the pipeline could involve multiple kind of tests (I - III):
I.Pairwise testing of combinations
II.Testing each individual model for accuracy
III.A/B testing of different sequences of models
Which ONE of the following options contains the kinds of tests that would be MOST APPROPRIATE to include in the strategy for optimal detection?
SELECT ONE OPTION

A) I and III
B) I and II
C) Only II
D) Only III


2. Which ONE of the following options is an example that BEST describes a system with Al-based autonomous functions?
SELECT ONE OPTION

A) A system that utilizes human beings for all important decisions.
B) A system that is fully able to respond to its environment.
C) A fully automated manufacturing plant that uses no software.
D) A system that utilizes a tool like Selenium.


3. Pairwise testing can be used in the context of self-driving cars for controlling an explosion in the number of combinations of parameters.
Which ONE of the following options is LEAST likely to be a reason for this incredible growth of parameters?
SELECT ONE OPTION

A) Different weather conditions
B) Different features like ADAS, Lane Change Assistance etc.
C) ML model metrics to evaluate the functional performance
D) Different Road Types


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


Solutions:

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

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