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Last Updated: Sep 13, 2026
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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Risks and Testing Challenges for Generative AI | 30% | - Testing Challenges for Generative AI
|
| Topic 2: Tools for Testing Generative AI | 20% | - Using Tools for Common Testing Activities
|
| Topic 3: Testing Activities for Generative AI | 30% | - Traceability and Documentation
|
| Topic 4: Fundamentals of Generative AI | 20% | - Generative AI Concepts
|
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.
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Which factor MOST influences the overall energy consumption of a Generative AI model used in software testing tasks?
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Which technique MOST directly reduces hallucinations by grounding the model in project realities?
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The model flags anomalies in logs and also proposes partitions for input validation tests. Which metrics BEST evaluate these two outcomes together?
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An LLM prioritizes tests using likelihood X impact but ranks a trivial tooltip change above a payment failure.
What defect does this MOST LIKELY show?
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