ISQI CT-GenAI

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

2. What BEST protects sensitive test data at rest and in transit?

A) Enforce role-based access controls
B) Disable TLS and rely on VPN only
C) Rely on obfuscation instead of encryption
D) Use public file shares with read-only links

3. 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) Zero-shot prompting
B) Prompt chaining
C) Few-shot prompting
D) Meta prompting

4. An LLM prioritizes tests using likelihood X impact but ranks a trivial tooltip change above a payment failure.
What defect does this MOST LIKELY show?

A) Dataset bias toward UI features
B) Hallucination
C) Reasoning error in risk calculation logic
D) No defect; this is acceptable

5. 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) iv -> iii -> i -> ii
C) i -> iv -> iii -> ii
D) iv -> i -> iii -> ii

Solutions: