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

1.1.4 During a training exercise, a team is instructed to review and execute a prompt for a multimodal LLM. They must first inspect both an application screenshot and its textual requirements, then execute the prompt and verify results. What is the main goal of this exercise?

Options :

A : To practice manual visual comparison of UI elements

B : To demonstrate combined text and image input processing for testing

C : To fine-tune a vision-language model for a new dataset

D : To convert all visual data to textual descriptions before analysis

Answer: B

Question 3

4.1.1 Which statement best describes why the architecture goes beyond a traditional client-server model?

Options :

A : It uses only in-house LLMs without external integration.

B : It replaces the server with multiple chatbots for redundancy.

C : It incorporates intelligent processing components like LLMs and multi-source back ends.

D : It eliminates the need for front-end and back-end separation.

Answer: C

Question 4

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 5

4.1.3 What is the main focus of the hands-on demonstration of an LLM-powered agent?

Options :

A : Measuring energy consumption compared to traditional automation.

B : Demonstrating input data, the agent’s behavior, and its resulting actions.

C : Comparing autonomous vs semi-autonomous performance.

D : Evaluating orchestration as a method to reduce hallucinations.

Answer: B

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