Answer-first summary for fast verification
Answer: Configure a Databricks Personal Access Token (PAT) for each user and validate it within the serving endpoint
## Explanation For Databricks model serving endpoints, the recommended approach for token-based authentication is to use **Databricks Personal Access Tokens (PAT)**. Here's why: - **PAT Authentication**: Databricks PATs are specifically designed for programmatic access to Databricks services, including model serving endpoints - **Built-in Security**: The serving endpoint infrastructure automatically validates PATs against Databricks authentication systems - **User Management**: You can create and manage PATs for individual users through the Databricks workspace - **Access Control**: PATs can be scoped with specific permissions and have expiration dates for enhanced security - **Integration**: This approach integrates seamlessly with Databricks' existing authentication and authorization infrastructure **Key Benefits:** - Centralized user management through Databricks workspace - Automatic token validation by the serving infrastructure - No need for custom authentication logic in your model code - Built-in security features like token expiration and scope limitations This is the standard and most secure method for controlling access to Databricks model serving endpoints using token-based authentication.
Author: LeetQuiz .
Ultimate access to all questions.
Question: 33
You have deployed a machine learning model on Databricks for serving through a REST API. To ensure that only authorized users can access the model serving endpoint, you decide to implement token-based authentication. Which of the following is the best approach to control access to the model serving endpoint using token-based authentication?
A
Configure a Databricks Personal Access Token (PAT) for each user and validate it within the serving endpoint
B
No comments yet.