DATABRICKS-DEA Question 58

Question

A junior data engineer proposes defining the gold-layer `customer_lifetime_value` object as a streaming table, reasoning that customers table updates frequently and a streaming table processes new records efficiently. The transformation logic recalculates lifetime value by aggregating a customer's full historical order history and applying corrections whenever late-arriving refund records are back-dated into earlier months. Why is a streaming table a poor fit for this specific gold-layer object?
A. A streaming table applies exactly-once, append-only processing to each record, so it cannot handle logic that must reprocess aggregated historical values or update earlier results because of back-dated refund corrections.
B. A streaming table cannot be created inside a Lakeflow Spark Declarative Pipeline at all, so any attempt to define `customer_lifetime_value` as a streaming table would immediately fail to compile or deploy within that pipeline.
C. A streaming table always stores its data in the internal `__databricks_internal` catalog rather than a user-facing schema, making it impossible for BI tools to connect to or query the resulting lifetime value numbers directly.
D. A streaming table can only be defined using Python decorators and does not support SQL syntax at all, so the team would be forced to rewrite the entire lifetime-value aggregation logic in PySpark instead of SQL.

Accepted Answer

Marked answer: A. A streaming table applies exactly-once, append-only processing to each record, so it cannot handle logic that must reprocess aggregated historical values or update earlier results because of back-dated refund corrections.

DATABRICKS-DEA Question 58

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DATABRICKS-DEA Question 58

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