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

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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. You must generate test cases for a new payments rule. The system includes API specifications stored in a vector database and prior tests in a relational database. Which of the following sequences BEST represents the correct order for applying a Retrieval-Augmented Generation (RAG) workflow?
i. Retrieve semantically similar specification chunks from the vector database ii. Feed both retrieved datasets as context for the LLM to generate new test cases iii. Retrieve relevant historical cases from the relational database iv. Submit a focused query describing the new test requirement

A) iv -> iii -> i -> ii
B) iii -> iv -> i -> ii
C) iv -> i -> iii -> ii
D) i -> iv -> iii -> ii

2. 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

3. What is a primary compliance concern related to Shadow AI in organizational test environments?

A) Failure to update system documentation within the test process
B) Violation of established data handling and regulatory compliance standards
C) Automated compliance validation during AI tool deployment
D) Difficulty in aligning project milestones with business outcomes

4. Which option BEST differentiates the three prompting techniques?

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

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

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

Solutions: