Question 1

1.1.3 A test automation team integrates an LLM into their defect triaging workflow. The initial implementation uses a model pre-trained on vast text, code, and image datasets without any fine-tuning. The team notices that while the model can categorize defects across multiple domains, it often fails to follow precise categorization rules provided in the prompt. Which type of LLM is most likely being used?

Options :

A : Instruction-tuned LLM

B : Reasoning LLM

C : Foundation LLM

D : Domain-specific symbolic AI

Answer: C

Question 2

4.1.1 In the architecture of an LLM-powered test infrastructure, which component manages authentication, data retrieval, and prompt preparation?

Options :

A : Front-end

B : Back-end

C : LLM model

D : External data sources

Answer: B

Question 3

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?

Options :

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

Question 4

3.1.3 A team formats the test data fed into an LLM in CSV with clearly labeled columns instead of using a loosely formatted text file. Which risk reduction method is being used?

Options :

A : Select the appropriate GenAI model

B : Provide complete context

C : Compare results across models

D : Use clear, interpretable data formats

Answer: D

Question 5

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?

Options :

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

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