Explanation:
The correct answer is C: The customers table is a streaming live table. The STREAM() wrapper is used when reading from a source that should be treated as an incremental stream in a streaming live table query. stackoverflow
In Databricks SQL, STREAM(LIVE.<table>) tells the pipeline to read the upstream live table as a streaming source rather than as a static snapshot. This is why it appears in CREATE STREAMING LIVE TABLE ... AS SELECT ... statements when the input should be processed incrementally. docs.azure
STREAM() is valid in this context and does not inherently cause an error. stackoverflowSTREAM(); the wrapper is about how the input is read. docs.databricksSTREAM() is not used to indicate that data changed since the last run; it indicates streaming/incremental ingestion behavior. learn.microsoftIf you see STREAM(LIVE.some_table), think: “read this upstream live table as a streaming source.” docs.databricks
Ultimate access to all questions.
A data engineer has joined an existing project and they see the following query in the project repository:
CREATE STREAMING LIVE TABLE loyal_customers AS
SELECT customer_id -
FROM STREAM(LIVE.customers)
WHERE loyalty_level = 'high';
CREATE STREAMING LIVE TABLE loyal_customers AS
SELECT customer_id -
FROM STREAM(LIVE.customers)
WHERE loyalty_level = 'high';
Which of the following describes why the STREAM function is included in the query?
A
The STREAM function is not needed and will cause an error.
B
The table being created is a live table.
C
The customers table is a streaming live table.
D
The customers table is a reference to a Structured Streaming query on a PySpark DataFrame.
E
The data in the customers table has been updated since its last run.
