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ISTQB Certified Tester Testing with Generative AI (CT-GenAI) v1.0: CT-GenAI Exam
"ISTQB Certified Tester Testing with Generative AI (CT-GenAI) v1.0", also known as CT-GenAI exam, is a ISQI Certification. With the complete collection of questions and answers, ExamcollectionPass has assembled to take you through 42 Q&As to your CT-GenAI Exam preparation. In the CT-GenAI exam resources, you will cover every field and category in AI Testing Certification helping to ready you for your successful ISQI Certification.
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ISQI CT-GenAI Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Introduction to Generative AI for Software Testing | 15% | - Core concepts: Generative AI, LLMs, foundation models - Use cases across the testing lifecycle - Capabilities and limitations relevant to testing |
| Deploying and Integrating GenAI in Test Organisations | 15% | - Roles, skills, and team readiness - Strategy, governance, and adoption roadmap - Measuring value and continuous improvement |
| LLM-Powered Test Infrastructure | 10% | - Architecture and deployment considerations - AI agents and integration with test tools - RAG, fine-tuning, and model adaptation |
| Prompt Engineering for Effective Software Testing | 35% | - Iterative refinement and evaluation of prompts - Prompt patterns for test design, data generation, automation - Principles and structure of effective prompts |
| Managing Risks of Generative AI in Software Testing | 25% | - Hallucinations, bias, inaccuracy, and consistency risks - Validation, verification, and mitigation strategies - Data privacy, security, and compliance concerns |
ISQI ISTQB Certified Tester Testing with Generative AI (CT-GenAI) v1.0 Sample Questions:
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 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.
- B. 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.
- C. 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. - D. 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.
Correct Answer: C 🗳️
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Which factor MOST influences the overall energy consumption of a Generative AI model used in software testing tasks?
- A. The duration of user sessions primarily affects latency but not power efficiency
- B. The location of the data center determines model bias and accuracy levels
- C. The number of tokens processed directly determines the carbon intensity of each query
- D. The type of cloud platform affects processing speed but not total energy draw
Correct Answer: C 🗳️
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Your team needs to generate 500 API test cases for a REST API with 50 endpoints. You have documented 10 exemplar test cases that follow your organization's standard format. You want the LLM to generate test cases following the pattern demonstrated in your examples. Which of the following prompting techniques is BEST suited to achieve your goal in this scenario?
- A. Zero-shot prompting
- B. Few-shot prompting
- C. Prompt chaining
- D. Meta prompting
Correct Answer: B 🗳️
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Which of the following is NOT a valid form of LLM-driven test data generation?
- A. Creating combinatorial data (e.g., pairwise)
- B. Generating synthetic datasets
- C. Setting boundary values
- D. Creating production database backups
Correct Answer: D 🗳️
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Which setting can reduce variability by narrowing the sampling distribution during inference?
- A. Using a larger context window
- B. Increasing temperature
- C. Lowering temperature
- D. Increasing learning rate
Correct Answer: C 🗳️
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