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ISQI CT-GenAI Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Prompt Engineering for Effective Software Testing | 35% | - Iterative refinement and evaluation of prompts - Principles and structure of effective prompts - Prompt patterns for test design, data generation, automation |
| Introduction to Generative AI for Software Testing | 15% | - Use cases across the testing lifecycle - Core concepts: Generative AI, LLMs, foundation models - Capabilities and limitations relevant to testing |
| LLM-Powered Test Infrastructure | 10% | - RAG, fine-tuning, and model adaptation - AI agents and integration with test tools - Architecture and deployment considerations |
| Deploying and Integrating GenAI in Test Organisations | 15% | - Measuring value and continuous improvement - Strategy, governance, and adoption roadmap - Roles, skills, and team readiness |
| Managing Risks of Generative AI in Software Testing | 25% | - Validation, verification, and mitigation strategies - Hallucinations, bias, inaccuracy, and consistency risks - Data privacy, security, and compliance concerns |
ISQI ISTQB Certified Tester Testing with Generative AI (CT-GenAI) v1.0 Sample Questions:
1. Which setting can reduce variability by narrowing the sampling distribution during inference?
A) Increasing temperature
B) Using a larger context window
C) Lowering temperature
D) Increasing learning rate
2. Which concept refers to breaking text into smaller units for processing by LLMs?
A) Embeddings
B) Transformer
C) Context Window
D) Tokenization
3. Which of the following is NOT a valid form of LLM-driven test data generation?
A) Creating combinatorial data (e.g., pairwise)
B) Setting boundary values
C) Generating synthetic datasets
D) Creating production database backups
4. 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. | 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 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.
D) 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.
5. An attacker sends extremely long prompts to overflow context so the model leaks snippets from its training data. Which attack vector is this?
A) Request manipulation
B) Data poisoning
C) Data exfiltration
D) Malicious code generation
Solutions:
| Question # 1 Answer: C | Question # 2 Answer: D | Question # 3 Answer: D | Question # 4 Answer: D | Question # 5 Answer: C |
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