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    Free Practice Questions for ISTQB Certified Tester – Testing with Generative AI (CT-GenAI) Certification

    Guide checked for updates:
    19 Sep 2026
    Question bank created:
    15 May 2026
    Question bank last updated:
    6 Oct 2026

    Study with 348 exam-style practice questions designed to help you prepare for the ISTQB Certified Tester – Testing with Generative AI (CT-GenAI). 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 – Testing with Generative AI (CT-GenAI)

    Official study guide

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    Question formats CertSafari offers
    • Multiple choice
    passing score:

    30 (65%)

    prerequisites:

    ISTQB Certified Tester Foundation Level (CTFL)

    target audience:

    Testers, test analysts, test automation engineers, test managers, user-acceptance testers, software developers, project managers, quality managers, software development managers, business analysts, IT directors, and 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 Generative AI for Software Testing

    1.1: Generative AI Foundations and Key Concepts

    1.2: Leveraging Generative AI in Software Testing: Core Principles

    2: Prompt Engineering for Effective Software Testing

    2.1: Effective Prompt Development

    2.2: Applying Prompt Engineering Techniques to Software Test Tasks

    2.3: Evaluate Generative AI Results and Refine Prompts for Software Test tasks

    3: Managing Risks of Generative AI in Software Testing

    3.1: Hallucinations, Reasoning Errors and Biases

    3.2: Data Privacy and Security Risks of Generative AI in Software Testing

    3.3: Energy Consumption and Environmental Impact of Generative AI in Software Testing

    3.4: AI Regulations, Standards, and Best Practice Frameworks

    4: LLM-Powered Test Infrastructure for Software Testing

    4.1: Architectural Approaches for LLM-Powered Test Infrastructure

    4.2: Fine-Tuning and LLMOps: Operationalizing Generative AI for Software Testing

    5: Deploying and Integrating Generative AI in Test organizations

    5.1: Roadmap for the Adoption of Generative AI in Software Testing

    5.2: Manage Change when Adopting Generative AI for Software Testing

    Techniques & products

    Generative AI (GenAI)
    Large Language Models (LLMs)
    Software Testing
    Prompt Engineering
    Requirements Analysis
    Test Design
    Test Automation
    Test Reporting
    Continuous Improvement
    Hallucinations
    Reasoning Errors
    Biases
    Data Privacy
    Security Risks
    Energy Consumption
    Environmental Impact
    AI Regulations
    Standards
    Best Practice Frameworks
    LLM-Powered Testing Solutions
    Architectural Approaches
    Fine-Tuning
    LLMOps
    GenAI Adoption Roadmap
    Change Management

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