Google Professional Machine Learning Engineer
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In your role as a Machine Learning Engineer at a large enterprise, you are tasked with developing a model on AI Platform. The company has thousands of datasets stored in BigQuery, each with accurate descriptions. Given the scale of data, efficiency and accuracy in locating the right BigQuery table for your model are paramount. Additionally, the solution must adhere to the company's policy of minimizing manual maintenance and leveraging Google Cloud's native services for scalability. Which of the following methods is the most efficient and scalable to locate the necessary data? (Choose one correct option)
A
Tag each model and version resource on AI Platform with the corresponding BigQuery table name used for training, ensuring traceability but requiring manual updates for new tables.
B
Query BigQuery's INFORMATION_SCHEMA metadata tables to list all table names in your project and manually identify the needed table, a method that is straightforward but not scalable for thousands of datasets._
C
Utilize Data Catalog to search BigQuery datasets by keywords found in table descriptions, leveraging Google Cloud's native service for metadata management and search.
D
Create and maintain a BigQuery lookup table that links table descriptions to table IDs, then query this lookup table to find the correct table ID, a solution that introduces maintenance overhead.