Databricks Certified Data Engineer - Professional
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A data engineer is designing the schema for a Delta Lake table named silver_device_recordings. This table stores complex, highly nested JSON data containing 100 unique fields, only 45 of which are currently required for downstream applications. When choosing between manual schema declaration and schema inference, which factor is the most critical to consider in a Databricks environment?
A
Delta Lake's use of Parquet allows for easy data type evolution by modifying file footer information, bypassing the need for data rewrites.
B
Manual schema declaration ensures higher data quality and stricter enforcement compared to inference, as Databricks' inference engine defaults to the widest compatible data types to accommodate all observed data.
C
Databricks' Tungsten engine is specifically optimized for raw JSON string storage, making it more efficient to store the entire JSON object as a string rather than defining a nested schema.
D
In migration workflows, the automation of table declaration logic is the highest priority because human labor is the most significant expense in data engineering.
E
Schema inference and evolution in Databricks are designed to guarantee that inferred types will automatically match the specific data type expectations of downstream analytical tools.