Support for T SQL based analytics that will be familiar to SQL Server developers is correct because Azure Synapse offers a native T SQL query surface that aligns with SQL Server conventions and tools.

Azure Synapse provides both dedicated SQL pools and serverless SQL pools that support T SQL syntax and familiar SQL Server constructs such as views and stored procedures. This reduces friction for SQL Server developers who want to run analytics without learning a different query language or execution model.

Databricks is built around Apache Spark and uses Spark SQL and other Spark APIs. While Databricks can run SQL queries it does not provide the same native T SQL compatibility and SQL Server oriented tooling that Synapse delivers.

Capability to dynamically scale compute resources to meet changing workloads is incorrect because both Synapse and Databricks support autoscaling and flexible compute provisioning, so this capability is not unique to Synapse.

Native connector integration with Google BigQuery is incorrect because integration with external systems like BigQuery is not a unique Synapse capability and both platforms can connect to external data sources through connectors or integration tools.

Ability to execute distributed parallel processing across clusters is incorrect because distributed parallel processing is a core capability of Spark and of Synapse distributed query engines, so it does not uniquely differentiate Synapse from Databricks.

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