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

1. Which consideration BEST aligns LLM choice with organizational goals in a GenAI testing strategy?

A) Select open-source models prioritizing creativity over compliance or performance consistency
B) Select broad-coverage models offering diverse functionalities for various test scenarios
C) Select models with maximum vendor visibility and strong online presence to ensure reliability
D) Select LLMs aligned to measurable test outcomes, compatible with current infrastructure

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) Malicious code generation
B) Data exfiltration
C) Request manipulation
D) Data poisoning

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

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 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 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.

5. 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) Accuracy for anomaly detection and Precision for coverage of valid/invalid partitions
C) Precision for anomaly identification and recall for coverage of valid/invalid partitions
D) Time efficiency for anomaly detection and accuracy for coverage of valid/invalid partitions

Solutions: