ISQI CT-GenAI : ISTQB Certified Tester Testing with Generative AI (CT-GenAI) v1.0

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ISQI ISTQB Certified Tester Testing with Generative AI (CT-GenAI) v1.0 Sample Questions:

1. Which statement about fine-tuning for test tasks is INCORRECT?

A) It enhances relevance to organizational terminology and formats
B) It can be applied to smaller SLMs to improve task performance with lower compute
C) It replaces the model's general knowledge entirely and prevents overfitting
D) It adapts a pre-trained model to a domain using task-specific data

2. A team notices vague, inconsistent LLM outputs for the same story for two different prompts. Which technique BEST helps choose the stronger wording among two prompt versions using predefined metrics?

A) Integrating user feedback
B) A/B testing of prompts
C) Iterative prompt modification
D) Output analysis

3. What BEST protects sensitive test data at rest and in transit?

A) Disable TLS and rely on VPN only
B) Use public file shares with read-only links
C) Rely on obfuscation instead of encryption
D) Enforce role-based access controls

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 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.
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 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.
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.

5. Which option BEST differentiates the three prompting techniques?

A) Few-shot = no examples; Chaining = single prompt; Meta = disable iteration
B) Chaining = give examples; Few-shot = break tasks; Meta = manual edits only
C) Meta = step decomposition; Chaining = zero-shot only; Few-shot = manual optimization
D) Few-shot = examples; Chaining = multi-step prompts; Meta = model helps draft/refine prompts

Solutions: