Updated: Jul 03, 2026
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1. Which technique MOST directly reduces hallucinations by grounding the model in project realities?
A) Provide detailed context
B) Randomize prompts each run
C) Rely on generic examples only
D) Use longer temperature settings
2. Which consideration BEST aligns LLM choice with organizational goals in a GenAI testing strategy?
A) Select models with maximum vendor visibility and strong online presence to ensure reliability
B) Select LLMs aligned to measurable test outcomes, compatible with current infrastructure
C) Select broad-coverage models offering diverse functionalities for various test scenarios
D) Select open-source models prioritizing creativity over compliance or performance consistency
3. In the context of software testing, which statements (i-v) about foundation, instruction-tuned, and reasoning LLMs are CORRECT?
i. Foundation LLMs are best suited for broad exploratory ideation when test requirements are underspecified.
ii. Instruction-tuned LLMs are strongest at adhering to fixed test case formats (e.g., Gherkin) from clear prompts.
iii. Reasoning LLMs are strongest at multi-step root-cause analysis across logs, defects, and requirements.
iv. Foundation LLMs are optimal for strict policy compliance and template conformance.
v. Instruction-tuned LLMs can follow stepwise reasoning without any additional training or prompting.
A) i, ii, iii (Duplicate entry in original source)
B) i, iii, v
C) i, ii, iii
D) ii, iii, iv
4. Which statement about fine-tuning for test tasks is INCORRECT?
A) It enhances relevance to organizational terminology and formats
B) It can be applied to smaller SLMs to improve task performance with lower compute
C) It adapts a pre-trained model to a domain using task-specific data
D) It replaces the model's general knowledge entirely and prevents overfitting
5. 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) Meta prompting
B) Few-shot prompting
C) Zero-shot prompting
D) Prompt chaining
Solutions:
| Question # 1 Answer: A | Question # 2 Answer: B | Question # 3 Answer: C | Question # 4 Answer: D | Question # 5 Answer: B |
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