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

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

2. 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 dynamically generates test insights using contextual information
C) It focuses primarily on visual dashboards and user navigation features
D) It produces scripted conversational responses similar to traditional bots

3. 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, iii, v
C) i, ii, iii (Duplicate entry in original source)
D) i, ii, iii

4. You are tasked with applying structured prompting to perform impact analysis on recent code changes. Which of the following improvements would BEST align the prompt with structured prompt engineering best practices for comprehensive impact analysis?

A) Specify that the role is a test architect specializing in CI/CD pipelines.
B) Add a step to review the change log for syntax errors before analysis.
C) Include references to version control systems like Git in the constraints.
D) Include mapping code changes to affected modules, identifying test cases, prioritizing by risk level and change complexity

5. 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

Solutions: