Free Practice ISTQB CT-GenAI Exam Questions 2026

Stay ahead with 100% Free Certified Tester Testing with Generative AI (CT-GenAI) CT-GenAI Dumps Practice Questions

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Total 224 Questions | Updated On: Mar 12, 2026

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Question 1

4.2.1 During fine-tuning of an LLM for test generation, the training dataset contains inconsistencies: some user stories are incomplete, and some test cases do not match the described functionality. After fine-tuning, the model frequently generates irrelevant or biased test cases. Which fine-tuning challenge does this illustrate?

A : Overfitting caused by small dataset size.

B : Opacity in the model’s reasoning.

C : Computational overhead in processing large models.

D : Poor training data quality.

Answer: D

Question 2

1.2.2 A tester is new to the team and needs quick clarifications on exploratory test approaches for a mobile banking app. They want answers immediately, without setting up any additional tools. What is the most efficient approach?

A : Integrate LLM API into the existing test management system for automated responses

B : Use an AI chatbot with prompt chaining for quick, conversational guidance

C : Build a custom LLM-powered tool for exploratory testing automation

D : Write manual documentation for exploratory testing steps

Answer: B

Question 3

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

A : It adapts a pre-trained model to a domain using task-specific data

B : It replaces the model’s general knowledge entirely and prevents overfitting

C : It enhances relevance to organizational terminology and formats

D : It can be applied to smaller SLMs to improve task performance with lower compute

Answer: B

Question 4

1.1.2 A performance test uses an SLM to provide quick insights on defect clustering. The model’s outputs are fast but miss subtle context in multi-sentence defect reports. The team wants minimal increase in processing time while improving comprehension. Which change is most suitable?

A : Replace SLM with LLM despite increased computational time

B : Integrate embeddings fine-tuned on historical defect data

C : Expand tokenization to the smallest possible tokens

D : Increase the model’s temperature parameter for output variety

Answer: B

Question 5

1.1.4 A test engineer needs to identify mismatches between the expected UI layout described in user stories and screenshots from the latest build. They want to maximize test coverage by also generating additional test cases using the same model. Which approach is most suitable?

A : Use a multimodal LLM with a vision-language module

B : Use a reasoning LLM with chain-of-thought enabled

C : Use separate models for text and images, then combine results manually

D : Use a standard transformer trained only on text data

Answer: A

Page: 1 / 45

Total 224 Questions | Updated On: Mar 12, 2026

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