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

1. What does an embedding represent in an LLM?

A) A set of test cases for validation
B) Logical rules for reasoning
C) Tokens grouped into context windows
D) Numerical vectors capturing semantic relationships

2. 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 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 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 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.

3. You are using an LLM to assist in analyzing test execution trends to predict potential risks. Which of the following improvements would BEST enhance the LLM's ability to predict risks and provide actionable alerts?

A) Specify that the role is a test analyst with expertise in predictive analytics and risk management.
B) Emphasize constraints that focus on deviations that could impact release timelines or quality gates.
C) Expand the output format to include risk predictions with severity levels, recommended actions, and a timeline for team intervention based on trend analysis.
D) Add an instruction to calculate statistical variance and highlight tests that deviate by more than 20% from baseline metrics.

4. You must use GenAI to perform test analysis on a payments module with finalized requirements: (1) generate test conditions, (2) prioritize by risk, (3) check coverage gaps. Which sequence best applies prompt chaining?

A) Generate conditions -> prioritize by risk -> map to requirements to find gaps
B) Prioritize requirements -> generate conditions -> review defects
C) Detect requirement defects -> generate conditions -> prioritize
D) Generate prioritized conditions in one shot -> verify coverage

5. Which of the following is NOT a valid form of LLM-driven test data generation?

A) Generating synthetic datasets
B) Setting boundary values
C) Creating combinatorial data (e.g., pairwise)
D) Creating production database backups

Solutions: