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GCP Professional Data Engineer Practice Question

A manufacturing company streams millisecond-level telemetry (temperature, pressure, event_timestamp, machine_id) from thousands of machines into BigQuery. Analysts must calculate mean time between failures per plant and quarter, enrich results with machine specifications (model, install_date) and plant attributes (country, business_unit), and frequently filter on event_timestamp ranges. To balance query performance, storage cost, and the ability to update machine or plant attributes without rewriting historical events, how should you map these business requirements to a warehouse data model?

  • Store telemetry in a fact table partitioned on event_timestamp and clustered by machine_id; place machine specifications and plant attributes in separate dimension tables referenced through surrogate keys.

  • Use a single table where machine and plant metadata are stored as nested STRUCTs inside every telemetry event record.

  • Create a single denormalized table that repeats all machine and plant attributes in every telemetry row to eliminate joins.

  • Model machine specifications as the fact table and treat each telemetry event as a dimension record linked to it.

GCP Professional Data Engineer
Storing the data
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