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ISQI CT-AI_v1.0_World Exam Syllabus Topics:

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

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

        1. Which ONE of the following characteristics is the least likely to cause safety related issues for an Al system?
        SELECT ONE OPTION

        A) Non-determinism
        B) Self-learning
        C) High complexity
        D) Robustness


        2. Which ONE of the following options describes the LEAST LIKELY usage of Al for detection of GUI changes due to changes in test objects?
        SELECT ONE OPTION

        A) Using a ML-based classifier to flag if changes in GUI are to be flagged for humans.
        B) Using a computer vision to compare the GUI before and after the test object changes.
        C) Using a vision-based detection of the GUI layout changes before and after test object changes.
        D) Using a pixel comparison of the GUI before and after the change to check the differences.


        3. Which ONE of the following hardware is MOST suitable for implementing Al when using ML?
        SELECT ONE OPTION

        A) High powered CPUs.
        B) Hardware supporting high precision floating point operations.
        C) Hardware supporting fast matrix multiplication.
        D) 64-bit CPUs.


        4. Which ONE of the following tests is MOST likely to describe a useful test to help detect different kinds of biases in ML pipeline?
        SELECT ONE OPTION

        A) Test the model during model evaluation for data bias.
        B) Testing the distribution shift in the training data for inappropriate bias.
        C) Check the input test data for potential sample bias.
        D) Testing the data pipeline for any sources for algorithmic bias.


        Solutions:

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

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