Databricks Updated Databricks-Certified-Professional-Data-Engineer Exam Questions and Answers by cohen

This code is using the pyspark.sql.functions library to group the silver_customer_sales table by customer_id and then aggregate the data using the minimum sale date, maximum sale total, and sum of distinct order ids. The resulting aggregated data is then written to the gold_customer_lifetime_sales_summary table, overwriting any existing data in that table. This is a batch job that does not use any incremental or streaming logic, and does not perform any merge or update operations. Therefore, the code will overwrite the gold table with the aggregated values from the silver table every time it is executed.  References :

https://docs.databricks.com/spark/latest/dataframes-datasets/introduction-to-dataframes-python.html

https://docs.databricks.com/spark/latest/dataframes-datasets/transforming-data-with-dataframes.html

https://docs.databricks.com/spark/latest/dataframes-datasets/aggregating-data-with-dataframes.html

Exact extract: “If no trigger is specified, the default processing-time trigger runs micro-batches as fast as possible.”

Exact extract: “Trigger once processes all available data once and then stops.”

Granting a user ' Can Read ' permissions on a notebook within Databricks allows them to view the notebook ' s content without the ability to execute or edit it. This level of permission ensures that the new team member can review the production logic for learning or auditing purposes without the risk of altering the notebook ' s code or affecting production data and workflows. This approach aligns with best practices for maintaining security and integrity in production environments, where strict access controls are essential to prevent unintended modifications.