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DP-700 - Ingest and Transform Data - Section 2.6

Create and manage OneLake shortcuts, and implement mirroring of external databases.

Create OneLake shortcuts to ADLS Gen2, Amazon S3, and internal Fabric items without copying data, and configure shortcut security and delegation. Contrast shortcuts with database mirroring for Azure SQL, Snowflake, and Cosmos DB, which provides near-real-time replication into OneLake.

OneLake shortcuts to ADLS Gen2, S3, and internal itemsshortcuts versus copying datadatabase mirroring (Azure SQL, Snowflake, Cosmos DB)near-real-time replicationshortcut security and delegation

Practice question for this objective

Free sampleIngest and Transform Datamedium

A team uses Snowflake as its operational analytics store and wants the same data available in Microsoft Fabric as Delta tables in OneLake that Spark and the SQL analytics endpoint can query. The replica should follow source changes closely, and the team wants a managed capability rather than a pipeline they have to build and operate. Which Fabric capability best meets these requirements?

  • AA OneLake shortcut from the Lakehouse to the Snowflake warehouse, so Fabric queries read the Snowflake tables in place without any replication.
  • BA Spark structured streaming job that continuously reads from Snowflake and writes Delta tables into the Lakehouse for the analytics endpoint.
  • CFabric mirroring for Snowflake, which replicates the Snowflake database into OneLake as Delta tables that Spark and the SQL analytics endpoint can query. Correct
  • DA scheduled Dataflows Gen2 refresh that reads Snowflake and writes the tables into the Lakehouse once an hour for downstream queries.
Use Fabric mirroring for Snowflake to land a managed, near-real-time Delta replica in OneLake instead of building a replication pipeline. Mirroring supports Snowflake as a source and continuously replicates its tables into OneLake in Delta format, so Spark and the SQL analytics endpoint can query a replica that closely follows the source; because the service manages the replication, the team avoids building and operating a streaming or refresh pipeline of its own.

Why A is wrong: Shortcuts point at storage locations and supported item types, not a Snowflake compute warehouse, so this does not surface Snowflake tables as queryable OneLake Delta tables.

Why B is wrong: A structured streaming job could replicate the data, but it is a pipeline the team must build, tune, and operate, which contradicts the requirement for a managed capability with low overhead.

Why C is correct: Fabric mirroring supports Snowflake as a source and continuously lands the data in OneLake as Delta tables, giving the managed, near-real-time replica the requirement asks for without a hand-built pipeline.

Why D is wrong: An hourly Dataflows Gen2 refresh lags the source by up to an hour and is a schedule the team manages, so it neither follows changes closely nor removes the operational burden mirroring eliminates.

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