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

1. Which technique MOST directly reduces hallucinations by grounding the model in project realities?

A) Use longer temperature settings
B) Randomize prompts each run
C) Provide detailed context
D) Rely on generic examples only

2. A tester uploads crafted images that steer the LLM into validating non-existent acceptance criteria. Which attack vector is this?

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

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

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

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

5. Your team needs to generate 500 API test cases for a REST API with 50 endpoints. You have documented 10 exemplar test cases that follow your organization's standard format. You want the LLM to generate test cases following the pattern demonstrated in your examples. Which of the following prompting techniques is BEST suited to achieve your goal in this scenario?

A) Prompt chaining
B) Few-shot prompting
C) Zero-shot prompting
D) Meta prompting

Solutions: