You store and analyze your relational data in BigQuery on Google Cloud with all data that resides in US regions. You also have a variety of object stores across Microsoft Azure and Amazon Web…

Question

Options

• AUse BigQuery Data Transfer Service to load files from Azure and AWS into BigQuery.
• BCreate a Dataflow pipeline to ingest files from Azure and AWS to BigQuery.
• CLoad files from AWS and Azure to Cloud Storage with Cloud Shell gsutil rsync arguments.
• DUse the BigQuery Omni functionality and BigLake tables to query files in Azure and AWS.

• A
  25% (8)
• B
  9% (3)
• C
  6% (2)
• D
  59% (19)

Explanation

BigQuery Omni extends BigQuery's query engine to run directly against data stored in Azure and AWS object stores, meaning your data never leaves its original location - exactly what "as little movement of data as possible" demands. BigLake tables provide a unified governance layer over these cross-cloud sources, letting you query Azure Blob Storage and AWS S3 through standard BigQuery SQL as if the data were local.

A is wrong because the Data Transfer Service physically copies data into BigQuery - the opposite of minimizing movement. B is wrong because a Dataflow pipeline also ingests (moves) data into BigQuery; it adds engineering overhead and still violates the no-movement requirement. C is wrong because gsutil rsync copies files from Azure/AWS into Cloud Storage first, which is yet another unnecessary data transfer hop before BigQuery can even see the data.

Memory tip: "Omni" means everywhere - BigQuery Omni lets BigQuery reach everywhere (Azure, AWS) without pulling data back home. If the question mentions multiple clouds and minimizing data movement, Omni + BigLake is almost always the answer.

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