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

CT-AI_v1.0_World Exam Simulator
  • Exam Code: CT-AI_v1.0_World
  • Exam Name: ISTQB Certified Tester AI Testing (v1.0)
  • Updated: Aug 09, 2026
  • Q & A: 40 Questions and Answers

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ISQI CT-AI_v1.0_World Exam Syllabus Topics:

SectionWeightObjectives
Testing Quality Characteristics11%- Explainability and reliability testing
- Testing transparency, fairness, robustness
Neural Networks and Testing4%- Coverage measures for deep learning
- Structure of neural networks
Testing AI-Based Systems11%- Test strategy and approach
- Specific challenges and risks
ML Data10%- Data quality issues and impact
- Data acquisition, preprocessing, labeling
Test Environment for AI Systems2%- Data and infrastructure requirements
Introduction to AI10%- AI definitions and types
  • 1. Narrow, General and Super AI
    • 2. AI vs conventional systems
      - AI technologies and frameworks
      • 1. AI hardware and AIaaS
        Using AI for Testing Activities10%- Test case generation, defect prediction
        - Regression optimization, test analysis
        AI-Based System Testing Methods17%- Model validation and verification
        - Adversarial testing, bias testing
        Machine Learning (ML) Overview11%- ML workflow, overfitting, underfitting
        - Supervised, unsupervised, reinforcement learning
        ML Functional Performance Metrics11%- ROC, AUC, MSE, silhouette coefficient
        - Confusion matrix, accuracy, precision, recall
        Quality Characteristics for AI-Based Systems10%- Ethics, bias, transparency and safety
        - Flexibility, adaptability, autonomy

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

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


        2. A ML engineer is trying to determine the correctness of the new open-source implementation *X", of a supervised regression algorithm implementation. R-Square is one of the functional performance metrics used to determine the quality of the model.
        Which ONE of the following would be an APPROPRIATE strategy to achieve this goal?
        SELECT ONE OPTION

        A) Train various models by changing the order of input features and verify that the R-Square score of these models vary significantly.
        B) Drop 10% of the rows randomly and create another model and compare the R-Square scores of both the models.
        C) Compare the R-Square score of the model obtained using two different implementations that utilize two different programming languages while using the same algorithm and the same training and testing data.
        D) Add 10% of the rows randomly and create another model and compare the R-Square scores of both the model.


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


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

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


        Solutions:

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

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