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

1. Which option BEST differentiates the three prompting techniques?

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

2. What is a hallucination in LLM outputs?

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

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

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

4. Which concept refers to breaking text into smaller units for processing by LLMs?

A) Tokenization
B) Embeddings
C) Context Window
D) Transformer

5. Which competency MOST helps testers steer LLMs to produce useful, on-policy testware?

A) Mastering prompt engineering
B) Writing low-level device drivers
C) Configuring network routers
D) Designing custom CPU instructions

Solutions: