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

1. Which of the following is NOT a valid form of LLM-driven test data generation?

A) Creating combinatorial data (e.g., pairwise)
B) Generating synthetic datasets
C) Creating production database backups
D) Setting boundary values

2. 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) iii -> iv -> i -> ii
B) i -> iv -> iii -> ii
C) iv -> iii -> i -> ii
D) iv -> i -> iii -> ii

3. A prompt begins: "You are a senior test manager responsible for risk-based test planning on a payments platform." Which component is this?

A) Context
B) Instruction
C) Constraints
D) Role

4. What is a key data-related aspect when defining a GenAI strategy for testing?

A) Aggregate data from all available organizational repositories without filtration
B) Neglect legacy data sources as they provide limited immediate relevance to testing tasks
C) Prioritize accurate and relevant input data secured through defined quality procedures
D) Use only auto-generated synthetic data to avoid dependency on enterprise repositories

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

Solutions: