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

1. A team notices vague, inconsistent LLM outputs for the same story for two different prompts. Which technique BEST helps choose the stronger wording among two prompt versions using predefined metrics?

A) A/B testing of prompts
B) Output analysis
C) Iterative prompt modification
D) Integrating user feedback

2. In the context of software testing, which statements (i-v) about foundation, instruction-tuned, and reasoning LLMs are CORRECT?
i. Foundation LLMs are best suited for broad exploratory ideation when test requirements are underspecified.
ii. Instruction-tuned LLMs are strongest at adhering to fixed test case formats (e.g., Gherkin) from clear prompts.
iii. Reasoning LLMs are strongest at multi-step root-cause analysis across logs, defects, and requirements.
iv. Foundation LLMs are optimal for strict policy compliance and template conformance.
v. Instruction-tuned LLMs can follow stepwise reasoning without any additional training or prompting.

A) i, iii, v
B) i, ii, iii (Duplicate entry in original source)
C) ii, iii, iv
D) i, ii, iii

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

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

4. Which statement BEST differentiates an LLM-powered test infrastructure from a traditional chatbot system used in testing?

A) It dynamically generates test insights using contextual information
B) It provides fixed responses from predefined rule sets and scripts
C) It focuses primarily on visual dashboards and user navigation features
D) It produces scripted conversational responses similar to traditional bots

5. Which setting can reduce variability by narrowing the sampling distribution during inference?

A) Increasing learning rate
B) Using a larger context window
C) Lowering temperature
D) Increasing temperature

Solutions: