Answer-first summary for fast verification
Answer: All records in the current version of the source tables will be considered in the join operations. The matched records will overwrite the `students_courses_details` table.
The query reads three static Delta tables using the `spark.table()` function, which means that all records in the current version of these tables will be read and considered in the join operations. The `pyspark.sql.DataFrame.join()` function performs an inner join operation by default, so only matched records will be written to the target table. The data is written in 'overwrite' mode to the target table, which completely overwrites the table. References: [spark.table()](https://spark.apache.org/docs/latest/api/python/reference/pyspark.sql/api/pyspark.sql.DataFrameReader.table.html), [DataFrame.join()](https://spark.apache.org/docs/3.1.2/api/python/reference/api/pyspark.sql.DataFrame.join.html).
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The data engineering team has implemented a join logic between three Delta tables: students, courses, and enrollments. The final output is written to a table named students_courses_details. What happens each time this code block is executed?
A
Only newly added records to any of the source tables will be considered in the join operations. The matched records will overwrite the students_courses_details table.
B
All records in the current version of the source tables will be considered in the join operations. The matched records will overwrite the students_courses_details table.
C
These join operations are stateful, meaning that they will wait for unmatched records to be added to the source tables prior to calculating the results.
D
All records in the current version of the source tables will be considered in the join operations. The unmatched records will overwrite the students_courses_details table.
E
Only newly added records to any of the source tables will be considered in the join operations. The unmatched records will overwrite the students_courses_details table.