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ISQI CT-GenAI Exam Syllabus Topics:

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

Question 1

The model flags anomalies in logs and also proposes partitions for input validation tests. Which metrics BEST evaluate these two outcomes together?

A. Accuracy for anomaly detection and Precision for coverage of valid/invalid partitions
B. Precision for anomaly identification and recall for coverage of valid/invalid partitions
C. Time efficiency for anomaly detection and accuracy for coverage of valid/invalid partitions
D. Diversity for anomaly identification and precision for partitions

Question 2

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 exfiltration
C. Data poisoning
D. Malicious code generation

Question 3

What is a hallucination in LLM outputs?

A. A transient network failure during inference
B. Generation of factually incorrect content for the task
C. A systematic preference learned from data
D. A logical mistake in multi-step deduction

Question 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 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.

Question 5

An LLM prioritizes tests using likelihood X impact but ranks a trivial tooltip change above a payment failure.
What defect does this MOST LIKELY show?

A. Hallucination
B. Reasoning error in risk calculation logic
C. No defect; this is acceptable
D. Dataset bias toward UI features

Solutions: