You work for a global shipping company. You want to train a model on 40 TB of data to predict which ships in each geographic region are likely to cause delivery delays on any given day. The model will

Question

Options

• ABigQuery
• BCloud Bigtable
• CCloud Datastore
• DCloud SQL for PostgreSQL

• A
  70% (16)
• B
  9% (2)
• C
  4% (1)
• D
  17% (4)

Explanation

BigQuery is the right choice because it natively integrates with BigQuery ML (for training and running predictions at scale on 40 TB+ datasets) and has built-in geospatial functions (via ST_* functions for GeoJSON/geography types) - covering both requirements in a single managed service designed for analytical workloads.

Cloud Bigtable (B) is a high-throughput NoSQL store great for time-series and IoT telemetry ingestion, but it has no native ML or geospatial processing - you'd need to move data elsewhere to do predictions.

Cloud Datastore (C) is a transactional NoSQL document database suited for small-to-medium operational data; it scales poorly to 40 TB analytical workloads and lacks ML or geospatial capabilities.

Cloud SQL for PostgreSQL (D) supports PostGIS for geospatial queries, but it is an OLTP database not designed for petabyte-scale analytics or native ML model training, making it the wrong fit for this scale.

Memory tip: Think "BigQuery = Big Analytics + BigQuery ML + Big Geospatial." Whenever an exam question combines large-scale data, ML predictions, and geospatial processing in one sentence, BigQuery is almost always the answer.

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