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ISQI CT-GenAI Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Prompt Engineering for Effective Software Testing | 35% | - Prompt patterns for test design, data generation, automation - Principles and structure of effective prompts - Iterative refinement and evaluation of prompts |
| Topic 2: Managing Risks of Generative AI in Software Testing | 25% | - Data privacy, security, and compliance concerns - Validation, verification, and mitigation strategies - Hallucinations, bias, inaccuracy, and consistency risks |
| Topic 3: Deploying and Integrating GenAI in Test Organisations | 15% | - Roles, skills, and team readiness - Measuring value and continuous improvement - Strategy, governance, and adoption roadmap |
| Topic 4: LLM-Powered Test Infrastructure | 10% | - Architecture and deployment considerations - AI agents and integration with test tools - RAG, fine-tuning, and model adaptation |
| Topic 5: 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 |
ISQI ISTQB Certified Tester Testing with Generative AI (CT-GenAI) v1.0 Sample Questions:
What distinguishes an LLM-powered agent from a basic AI chatbot in test processes?
- A. Ability to trigger automated actions beyond conversation
- B. Use of a conversational tone and improved response personalization
- C. Reliance on predefined templates to generate short, factual answers
- D. Ability to respond to prompts without explicit user instructions
Correct Answer: A 🗳️
The model flags anomalies in logs and also proposes partitions for input validation tests. Which metrics BEST evaluate these two outcomes together?
- A. Diversity for anomaly identification and precision for partitions
- B. Precision for anomaly identification and recall for coverage of valid/invalid partitions
- C. Accuracy for anomaly detection and Precision for coverage of valid/invalid partitions
- D. Time efficiency for anomaly detection and accuracy for coverage of valid/invalid partitions
Correct Answer: B 🗳️
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Which technique MOST directly reduces hallucinations by grounding the model in project realities?
- A. Use longer temperature settings
- B. Provide detailed context
- C. Rely on generic examples only
- D. Randomize prompts each run
Correct Answer: B 🗳️
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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 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 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.
- 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 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.
Correct Answer: C 🗳️
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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. Include mapping code changes to affected modules, identifying test cases, prioritizing by risk level and change complexity
- B. Specify that the role is a test architect specializing in CI/CD pipelines.
- C. Add a step to review the change log for syntax errors before analysis.
- D. Include references to version control systems like Git in the constraints.
Correct Answer: A 🗳️
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