Exam GES-C01 Topic 1 Question 78 Discussion
Actual exam question for Snowflake's GES-C01 exam
Question #: 78
Topic #: 1
Question #: 78
Topic #: 1
A machine learning engineering team is evaluating two different configurations of a Retrieval Augmented Generation (RAG) application. uses for generation, while uses 'mistral-7b' with a refined prompt for the same task. They aim to compare the and 'groundedness' of the generated responses, as well as the efficiency of context retrieval. Which of the following steps are crucial for setting up AI Observability in Snowflake to facilitate a meaningful side-by-side comparison and assess these specific metrics?


Suggested Answer: A,C,D Vote an answer
Option A is correct because instrumenting the 'generate_answer' function with the 'GENERATION' span type is essential for correctly capturing and evaluating the LLM's output for metrics like 'answer_relevance' and 'groundednesS. Registering them as distinct ' TruApp' versions or runs allows for side-by-side comparisons. Option C is correct because instrumenting the retrieval component with 'RETRIEVAL' span type enables the calculation of 'context_relevance' , which directly assesses the quality of the search results and is crucial for RAG evaluation. Option D is correct as creating separate runs with specific configurations (like ' and explicitly computing desired metrics such as 'answer_relevance' and 'groundedness' is the standard way to set up systematic evaluations and comparisons in AI Observability. Option B is incorrect; while cross-region inference might be necessary for model availability, it doesn't directly enable the comparison 'feature' within AI Observability. Option E is incorrect because 'prompt_tokens' and 'completion_tokens' track cost, not directly the quality aspects like 'answer_relevance' and 'groundednesS , which are key for RAG performance evaluation.
by Elliot at Sep 07, 2026, 05:36 AM
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