Question 1

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 2

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

Question 3

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?

Options :

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 4

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 5

2.2.2 A team uses few-shot prompting with three prior Gherkin examples to transform a new user story into Given-When-Then scenarios. The output includes steps covering both functional and negative paths. Which benefit is most likely achieved here?

Options :

A : Improved execution speed

B : Increased test environment stability

C : Reduced defect leakage through production

D : Higher coverage precision

Answer: D

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