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A Generative AI Engineer is tasked with deploying an application that takes advantage of a custom MLflow Pyfunc model to return some interim results. How should they configure the endpoint to pass the secrets and credentials?
Correct Answer: C
Context: Deploying an application that uses an MLflow Pyfunc model involves managing sensitive information such as secrets and credentials securely. Explanation of Options: * Option A: Use spark.conf.set(): While this method can pass configurations within Spark jobs, using it for secrets is not recommended because it may expose them in logs or Spark UI. * Option B: Pass variables using the Databricks Feature Store API: The Feature Store API is designed for managing features for machine learning, not for handling secrets or credentials. * Option C: Add credentials using environment variables: This is a common practice for managing credentials in a secure manner, as environment variables can be accessed securely by applications without exposing them in the codebase. * Option D: Pass the secrets in plain text: This is highly insecure and not recommended, as it exposes sensitive information directly in the code. Therefore,Option Cis the best method for securely passing secrets and credentials to an application, protecting them from exposure.
