A data science team is deploying a custom real-time inference service for a fine-tuned LLM using Snowpark Container Services (SPCS). They have a Docker image in their Snowflake image repository. They need to define the service using a YAML specification file. Which of the following are ''essential'' components or configurations that must be included in the 'spec.yaml' file for a long- running service that uses this image, custom environment variables, and requires external access?
Correct Answer: A,B,E
Option A is correct because the 'container' block, specifying the 'name' of the container and the Docker 'image' path from the Snowflake registry, is fundamental for defining any containerized service in SPCS. Option B is correct as the 'endpoints' block, explicitly defining a 'name', -port', and setting 'public: true' , is essential for a long-running service that requires external access via a web browser or API. Option C is incorrect because the 'compute_poor is specified during the 'CREATE SERVICE SQL command, not within the 'spec.yaml' file itself. Option D is incorrect as a 'volumes' block, while potentially useful for persistent storage, is not listed as an essential component in the provided spec.yaml' examples for basic service deployment and is not explicitly mentioned as a required part of the specification file. Option E is correct as the 'env' block within a container definition is used to set custom environment variables, which is explicitly shown in the Jupyterlab example for configuring the application within the container.