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    Free Practice Questions for ISTQB Certified Tester AI Testing (CT-AI) V2.0 Certification

    Exam guide version:
    2.0 (2026/04/17)
    Guide checked for updates:
    19 Sep 2026
    Question bank created:
    15 May 2026
    Question bank last updated:
    23 Sep 2026

    Study with 348 exam-style practice questions designed to help you prepare for the ISTQB Certified Tester AI Testing (CT-AI) V2.0. All questions are aligned with the latest exam guide and include detailed explanations to help you master the material.

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    Exam Details

    Key information about ISTQB Certified Tester AI Testing (CT-AI) V2.0

    Official study guide

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    Question formats CertSafari offers
    • Multiple choice
    passing score:

    29 out of 44 points

    prerequisites:

    ISTQB Certified Tester Foundation Level (CTFL)

    target audience:

    Testers, test analysts, and test engineers, Test managers, Test consultants, Data analysts and data scientists, Software developers involved in developing AI-based systems, User acceptance testers, Project managers, Quality managers, Software development managers, Business analysts, IT directors and management consultants

    time limit minutes:

    60 minutes (75 minutes for non-native speakers)

    number of questions:

    40

    Exam Topics & Skills Assessed

    Skills measured (from the official study guide)

    1: Introduction to Artificial Intelligence

    1.1: Introduction to AI

    2: Quality Characteristics for AI-Based Systems

    2.1: Quality Characteristics for AI-Based Systems

    2.2: Acceptance Criteria for AI-Based Systems

    3: Machine Learning

    3.1: Introduction to Machine Learning

    3.2: Data for Machine Learning

    3.3: ML Functional Performance Metrics for Classification

    3.4: Neural Networks

    4: Testing AI-Based Systems

    4.1: Introduction to Testing AI-Based Systems

    4.2: Testing Generative AI and Large Language Models

    4.3: Test Levels and Machine Learning Systems

    5: Input Data Testing for Machine Learning Systems

    5.1: Input Data Testing for Machine Learning Systems

    6: Model Testing for Machine Learning Systems

    6.1: Model Testing for Machine Learning Systems

    7: Machine Learning Development Testing

    7.1: Machine Learning Development Testing

    Techniques & products

    Artificial Intelligence (AI)
    Machine Learning (ML)
    Generative AI
    Large Language Models (LLMs)
    AI-based systems
    Probabilistic behavior
    Non-determinism
    Data reliance
    Quality Characteristics (ISO/IEC 25059)
    Acceptance Criteria
    ML Functional Performance Metrics
    Classification
    Neural Networks
    Input Data Testing
    Model Testing
    ML Development Testing
    Test Levels
    Test Strategy
    Test Case Design
    Test Execution

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