Databricks Certified Data Engineer - Professional
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In a Databricks project, you are tasked with integrating multiple external APIs to fetch and process data. The project must adhere to strict security protocols and handle API rate limits efficiently to ensure uninterrupted data processing. Considering the need for security, scalability, and compliance with API usage policies, which of the following approaches is the BEST for securing API keys and access tokens, and managing API rate limits? Choose the single best option.
A
Store the API keys and access tokens in plain text within the notebooks and manually adjust request intervals to manage API rate limits, despite the security risks and potential for human error.
B
Utilize Databricks secrets management for secure storage of API keys and access tokens, and implement a retry mechanism with exponential backoff to efficiently handle API rate limits.
C
Develop a custom encryption solution for API keys and access tokens, store them in a shared storage accessible to all notebooks, and manually monitor API usage to avoid exceeding rate limits.
D
Pass API keys and access tokens as environment variables in the notebooks and rely on a basic script to pause requests when nearing rate limits, without implementing secure storage or advanced retry logic.