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Certified Tester Testing with Generative AI — Requirements & Free Practice

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Certified Tester Testing with Generative AI

Professional certification in using generative AI for software testing, offered through ASTQB and AT*SQA.

Licensing authority: American Software Testing Qualifications Board · Application information

Initial by examination
Applicability
Current official ISTQB and ASTQB materials adopt English syllabus v1.1 as of this investigation; unpublished certification, route and version effective-date boundaries remain null.
Award
Pass the CT-GenAI certification examination; 30 of 46 points is the published passing score.
Preparation
Accredited training or self-study; a training course is not mandatory.
Prerequisite
The ISTQB Certified Tester Foundation Level (CTFL) certificate must already be held before taking CT-GenAI; no experience substitution is stated in the current applicable sources. This existing prior qualification does not instruct a second CTFL examination sitting or create a CTFL production task.
Scheduling
Register through AT*SQA; use the purchased code within 365 days, without extensions. Choose a testing center or online proctored appointment. Online availability is throughout the week, 24 hours a day, subject to scheduling. Extended-time non-native-language candidates select EXT. Request special accommodations at least one month ahead; reschedule online more than 24 hours ahead and testing-center appointments more than 72 hours ahead; a $90 fee applies inside the rescheduling fee window.

Certified Tester Testing with Generative AI

Provider
ASTQB examination delivered through AT*SQA; ISTQB certification scheme
Format
Multiple-choice examination in English
Exam questions
Total questions: 40.
Time limit
60 minutes; 75 minutes for eligible non-native-language candidates
Passing score
30 of 46 points (at least 65%)
Examination outline
  • Introduction to Generative AI for Software Testing (17.5%)
    • Generative AI Foundations and Key Concepts
    • Leveraging Generative AI in Software Testing: Core Principles
  • Prompt Engineering for Effective Software Testing (27.5%)
    • Effective Prompt Development
    • Applying Prompt Engineering Techniques to Software Test Tasks
    • Evaluate Generative AI Results and Refine Prompts for Software Test Tasks
  • Managing Risks of Generative AI in Software Testing (25%)
    • Hallucinations, Reasoning Errors and Biases
    • Data Privacy and Security Risks of Generative AI in Software Testing
    • Energy Consumption and Environmental Impact of Generative AI in Software Testing
    • AI Regulations, Standards and Best Practice Frameworks
  • LLM-Powered Test Infrastructure for Software Testing (12.5%)
    • Architectural Approaches for LLM-Powered Test Infrastructure
    • Fine-Tuning and LLMOps: Operationalizing Generative AI for Software Testing
  • Deploying and Integrating Generative AI in Test Organizations (17.5%)
    • Roadmap for the Adoption of Generative AI in Software Testing
    • Manage Change when Adopting Generative AI for Software Testing
Examination sources