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Answer: Adopt Cloud Data Catalog to automatically extract metadata from Cloud Storage objects and BigQuery data.
The optimal solution is to use Cloud Data Catalog, which automatically extracts metadata from various sources like Cloud Storage, BigQuery, and others, streamlining dataset discovery. Cloud Logging is unsuitable for metadata collection, focusing instead on event logging. Cloud Fusion serves as an ETL tool, not for metadata extraction. Creating a custom metadata extraction tool, such as querying BigQuery metadata, demands excessive effort and upkeep compared to leveraging a managed service. For more details, visit [Cloud Data Catalog Overview](https://cloud.google.com/data-catalog/docs/concepts/overview).
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After migrating several data warehouses to BigQuery and utilizing Cloud Storage for machine learning data, a manufacturer faces challenges as ML engineers and data analysts struggle to locate necessary datasets. The CTO seeks your advice on minimizing this workload. What would you recommend?
A
Utilize Cloud Fusion to monitor files uploaded to Cloud Storage and datasets loaded into BigQuery.
B
Implement Cloud Logging to track files uploaded to Cloud Storage and datasets to BigQuery.
C
Query the metadata catalog of BigQuery and Cloud Storage, then store the results in a BigQuery table for SQL queries by ML engineers and data analysts.
D
Adopt Cloud Data Catalog to automatically extract metadata from Cloud Storage objects and BigQuery data.
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