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Preparing and Using Data for Analysis →mediumMultiple Choice

PDE Preparing and Using Data for Analysis Practice Question

A data engineer needs to query data across BigQuery (in Google Cloud) and Snowflake (in AWS) without moving the data. Which service should they use?

Answer choices

Why each option matters

Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.

Correct answer & explanation

✓

BigQuery Omni

BigQuery Omni allows querying data across multiple clouds using BigQuery's interface, with compute running in the respective cloud. Data stays in place.

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

  • ✗

    Dataflow

    Why it's wrong here

    Dataflow is a managed Apache Beam runner for batch and stream processing pipelines, not a federated query engine, so it cannot join BigQuery and Snowflake tables in place. It would be correct for transforming or moving data between systems, which the stem explicitly forbids.

  • ✗

    Cloud SQL

    Why it's wrong here

    Cloud SQL is a managed relational database for MySQL, PostgreSQL and SQL Server, so it stores its own data rather than querying BigQuery and Snowflake in place. It would suit lifting a self-managed relational workload, but not cross-cloud federated analytics without data movement.

  • ✗

    Vertex AI Feature Store

    Why it's wrong here

    Vertex AI Feature Store serves and manages machine learning features for training and online inference; it does not execute SQL across external warehouses. It would be the right choice for centralising feature definitions, but the stem requires querying BigQuery and Snowflake data without relocation.

  • ✓

    BigQuery Omni

    Why this is correct

    BigQuery Omni supports multi-cloud analytics without data movement.

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Written by Johnson Ajibi, MSc IT Security

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