Exam Name: ISTQB Certified Tester Testing with Generative AI (CT-GenAI) v1.0
Certification Provider: ISQI
Corresponding Certification: AI Testing
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Check Updated on: Sep 15, 2026
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ISQI CT-GenAI Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Prompt Engineering for Testing | - Prompt design techniques
|
| Organizational Adoption and Governance | - Enterprise GenAI adoption
|
| Risk, Quality, and Limitations of GenAI | - Risks in GenAI usage
|
| Foundations of Generative AI and LLMs | - Introduction to Generative AI in Software Testing
|
| Application of GenAI in Software Testing | - Practical use in testing workflows
|
ISQI ISTQB Certified Tester Testing with Generative AI (CT-GenAI) v1.0 Sample Questions:
Which AI approach requires feature engineering and structured data preparation?
- A. Classical Machine Learning
- B. Generative AI
- C. Symbolic AI
- D. Deep Learning
Correct Answer: A 🗳️
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You are tasked with applying structured prompting to perform impact analysis on recent code changes. Which of the following improvements would BEST align the prompt with structured prompt engineering best practices for comprehensive impact analysis?
- A. Specify that the role is a test architect specializing in CI/CD pipelines.
- B. Include mapping code changes to affected modules, identifying test cases, prioritizing by risk level and change complexity
- C. Include references to version control systems like Git in the constraints.
- D. Add a step to review the change log for syntax errors before analysis.
Correct Answer: B 🗳️
Which setting can reduce variability by narrowing the sampling distribution during inference?
- A. Increasing learning rate
- B. Increasing temperature
- C. Using a larger context window
- D. Lowering temperature
Correct Answer: D 🗳️
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Consider applying the meta-prompting technique to generate automated test scripts for API testing. You need to test a REST API endpoint that processes user registration with validation rules. Which one of the following prompts is BEST suited to this task?
- A. Role: Act as an automation tester. | Context: You are validating an API endpoint. | Instruction: Generate Python test scripts that send POST requests and validate responses. | Input Data: User credentials. | Constraints: Include basic scenarios with asserts. | Output Format: Provide organized scripts.
- B. Role: Act as a test automation engineer with API testing experience. | Context: You are verifying user registration that enforces field and format validation. | Instruction: Generate pytest scripts using requests for both positive (valid) and negative (invalid email, weak password, missing fields) cases. | Input Data: POST /api/register with validation rules for email and password length. | Constraints:
Include fixtures, clear assertions, and naming consistent with pytest. | Output Format: Return complete Python test files. - C. Role: Act as a test automation engineer. | Context: You are creating tests for a registration endpoint. | Instruction: Generate Python test scripts using pytest covering both valid and invalid inputs. | Input Data: POST /api/register with email and password. | Constraints: Follow pytest structure. | Output Format: Provide scripts.
- D. Role: Act as a software engineer. | Context: You are testing registration logic. | Instruction: Create Python scripts to verify endpoint behavior. | Input Data: POST /api/register with test users. | Constraints: Add checks for status codes. | Output Format: Deliver functional scripts.
Correct Answer: B 🗳️
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Which statement BEST differentiates an LLM-powered test infrastructure from a traditional chatbot system used in testing?
- A. It produces scripted conversational responses similar to traditional bots
- B. It provides fixed responses from predefined rule sets and scripts
- C. It focuses primarily on visual dashboards and user navigation features
- D. It dynamically generates test insights using contextual information
Correct Answer: D 🗳️
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