Databricks-Certified-Data-Engineer-Associate Exam Dumps : Databricks Certified Data Engineer Associate Exam

 The spark.sql method can be used to execute SQL queries programmatically and return the result as a DataFrame. The spark.sql method accepts a string argument that contains a valid SQL statement. The data engineer can use a formatted string literal (f-string) to insert the Python variable table_name into the SQL query. The other methods are either invalid or not suitable for running SQL queries. References: Running SQL Queries Programmatically, Formatted string literals, spark.sql

In Delta Live Tables (DLT), when configured to run in Continuous Pipeline Mode, particularly in a production environment, the system is designed to continuously process and update data as it becomes available. This mode keeps the compute resources active to handle ongoing data processing and automatically updates all datasets defined in the pipeline at predefined intervals. Once the pipeline is manually stopped, the compute resources are terminated to conserve resources and reduce costs. This mode is suitable for production environments where datasets need to be kept up-to-date with the latest data.

References:Databricks documentation on Delta Live Tables: Delta Live Tables Guide

 An external table is a table that is defined in the metastore and points to an existing location in the storage system. When you drop an external table, only the metadata is deleted from the metastore, but the data files are not deleted from the storage system. This is because external tables are meant to be shared by multiple applications and users, and dropping them should not affect the data availability. On the other hand, a managed table is a table that is defined in the metastore and also managed by the metastore. When you drop a managed table, both the metadata and the data files are deleted from the metastore and the storage system, respectively. This is because managed tables are meant to be exclusive to the application or user that created them, and dropping them should free up the storage space. Therefore, the correct answer is C, because the table was external and only the metadata was deleted when the table was dropped. References: Databricks Documentation - Managed and External Tables, Databricks Documentation - Drop Table