Google Professional Machine Learning Engineer
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Ultimate access to all questions.
In your role as a Machine Learning Engineer at a large enterprise with thousands of datasets across multiple projects, you are tasked with developing a model on AI Platform. The first step involves identifying the correct BigQuery table that contains the training data. The table descriptions are critical for understanding the data schema and content. Given the scale of data and the need for efficiency, which of the following methods is the MOST efficient and scalable to locate accurate descriptions of these tables? (Choose one)
A
Tag each model and version resource on AI Platform with the name of the BigQuery table used for training, ensuring traceability but requiring manual updates for each model version.
B
Maintain a centralized lookup table in BigQuery that maps detailed table descriptions to table IDs, then query this lookup table to find the required table ID, adding overhead for maintaining the lookup table.
C
Execute a query in BigQuery to retrieve all table names and descriptions in your project using the INFORMATION_SCHEMA metadata tables, then manually identify the needed table from the results, which may be time-consuming for thousands of tables._
D
Use Google Data Catalog to search for BigQuery datasets by keywords in the table description, leveraging its search capabilities and metadata management features for efficient discovery.