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ISQI CT-GenAI Exam Syllabus Topics:

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

1. 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) Meta prompting
D) Zero-shot prompting

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

3. Which statement BEST contrasts interaction style and scope?

A) Chatbots enable conversational interactions; LLM apps provide capabilities for defined test tasks.
B) Chatbots enforce fixed workflows; LLM apps support free-form exploration beneficial for software testing
C) Chatbots require API integration; LLM apps do not.
D) Both are identical aside from UI theme.

4. When an organization uses an AI chatbot for testing, what is the PRIMARY LLMOps concern?

A) Maintaining data privacy and minimizing security risks from external services
B) Achieving faster responses by reducing model checkpoints and updates
C) Focusing primarily on user experience improvements and response formatting
D) Maximizing scalability by deploying larger cloud-based LLM clusters

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

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

Solutions: