Detailed Explanation

Based on Azure Synapse Analytics architecture and metadata synchronization capabilities, the query will successfully return the value 24 (Option A). Here's the technical reasoning:

Why Option A (24) is Correct:

1. Metadata Synchronization Between Spark and SQL Pools:

   • When you create a table in a Spark pool using CREATE TABLE mytestdb.myParquetTable, Azure Synapse automatically synchronizes the metadata to the serverless SQL pool.
   • The one-minute delay is sufficient for this synchronization to complete, ensuring the table is visible to the SQL pool.

2. Schema Resolution with 'dbo':

   • In serverless SQL pools, Spark-created tables are automatically exposed in the dbo schema by default.
   • The reference mytestdb.dbo.myParquetTable correctly resolves to the table created in the Spark database.
   • This aligns with Microsoft's documented behavior where Spark tables become accessible as external tables in the dbo schema of the corresponding database in serverless SQL pools.

3. Case Insensitivity:

   • Serverless SQL pools in Azure Synapse are case-insensitive for table and column names by default.
   • The mixed-case table name myParquetTable in the query will successfully match the table created in Spark, regardless of case.

4. Data Retrieval Logic:

   • The inserted row contains EmployeeID: 24 and EmployeeName: 'Alice'.
   • The WHERE clause WHERE EmployeeName = 'Alice' correctly filters for this specific record.
   • The SELECT statement SELECT EmployeeID will return the value 24 from the matching row.

Why Other Options Are Incorrect:

• Option B (an error): This would only occur if there were actual connectivity issues, permission problems, or incorrect table references. However, the metadata synchronization and proper schema resolution make this scenario work correctly.

• Option C (a null value): This would only happen if no matching records were found, but we know a row with EmployeeName = 'Alice' exists with EmployeeID: 24.

Best Practices Confirmation:

This behavior follows Azure Synapse Analytics best practices where:

• Spark-created tables are automatically available in serverless SQL pools
• The dbo schema is the default for exposing Spark tables to SQL
• Metadata synchronization typically completes within seconds, making the one-minute wait more than sufficient
• Case-insensitive queries are supported by default in serverless SQL pools

The query execution follows the expected integration pattern between Spark and serverless SQL pools in Azure Synapse Analytics.