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ISQI 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: Aug 09, 2026

Q & A: 40 Questions and Answers

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ISQI CT-AI_v1.0_World Exam Syllabus Topics:

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

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


        2. Which ONE of the following activities is MOST relevant when addressing the scenario where you have more than the required amount of data available for the training?
        SELECT ONE OPTION

        A) Feature selection
        B) Data augmentation
        C) Data sampling
        D) Data labeling


        3. Which ONE of the following options does NOT describe an Al technology related characteristic which differentiates Al test environments from other test environments?
        SELECT ONE OPTION

        A) Challenges resulting from low accuracy of the models.
        B) Challenges in the creation of scenarios of human handover for autonomous systems.
        C) The challenge of mimicking undefined scenarios generated due to self-learning
        D) The challenge of providing explainability to the decisions made by the system.


        4. Which ONE of the following types of coverage SHOULD be used if test cases need to cause each neuron to achieve both positive and negative activation values?
        SELECT ONE OPTION

        A) Value coverage
        B) Neuron coverage
        C) Sign change coverage
        D) Threshold coverage


        Solutions:

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

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