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GCP Professional Data Engineer Practice Question

Your manufacturing company ingests several terabytes of IoT sensor data each day as partitioned Parquet files in a Cloud Storage data lake. Data engineering teams must give hundreds of analysts interactive SQL access while enforcing BigQuery row-level security policies and a unified audit trail. Management wants to avoid duplicating the data and minimise storage cost, but they still need BigQuery-style metadata caching for performance. Which architecture best satisfies these requirements?

  • Run a daily Dataflow pipeline that loads the Parquet files into partitioned BigQuery native tables and apply column- and row-level security there.

  • Load the Parquet data into Cloud Bigtable using Dataflow and query it from BigQuery through a Bigtable external connection.

  • Expose the bucket as a BigQuery external table and control access only through Cloud Storage IAM roles.

  • Create BigLake tables in BigQuery that reference the Parquet objects in Cloud Storage and apply row-level security policies on those tables.

GCP Professional Data Engineer
Storing the data
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