Package-tracking data streams into BigQuery; the table is partitioned by ingest date and query times have increased. To copy data to a new clustered table for geospatial lifecycle analysis, what should you cluster on?

Clustering on the package-tracking ID column is the most effective solution because geospatial lifecycle analysis likely involves queries that filter or aggregate data based on individual packages. Clustering by package-tracking ID co-locates rows with the same ID, significantly reducing the amount of data BigQuery needs to scan, thus improving query performance and reducing costs. Re-creating the table with partitioning on delivery date might help queries related to delivery dates, but it won't optimize queries focused on individual package journeys. Clustering on the ingest date column would be redundant since the table is already partitioned by ingest date; clustering on the same column as partitioning offers no additional performance benefit. Tiering older data to Cloud Storage and using an external table would likely degrade query performance due to the overhead of accessing external data and is not a direct optimization for the current BigQuery table's query performance issues.