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A data engineer is tasked with establishing AI Observability for a generative AI application that integrates with external systems and will undergo continuous improvement. The goal is to compare different iterations of the application efficiently. Which combination of configuration best practices, features, and governance aspects are most relevant for a robust setup of AI Observability within Snowflake for this scenario?
Correct Answer: A,B,C,D
Option A is correct because installing the specified TruLens Python packages (version 2.1.2 or later) and setting STRULENS OTEL TRACINGS to are prerequisites for instrumenting the application and enabling tracing for AI Observability. Option B is correct because the 'CORTEX_USER database role and application role are explicitly required for creating and executing runs for AI Observability. Option C is correct as the 'Comparisons' feature is a core component of AI Observability, designed precisely for assessing and comparing application quality, accuracy, and performance across various LLMs, prompts, and configurations. Option D is correct because AI Observability, like other Cortex LLM Functions, might require 'CORTEX ENABLED_CROSS REGION' to be configured if the service or specific LLMs are not natively available in the primary Snowflake region. Option E is incorrect because the sources explicitly state that you cannot run your project using the TruLens SDK in a Snowflake Notebook for AI Observability.