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Download DemoVendor: Databricks
Certifications: Databricks Certifications
Exam Code: DATABRICKS-CERTIFIED-PROFESSIONAL-DATA-ENGINEER
Exam Name: Databricks Certified Data Engineer Professional
Updated: Jan 04, 2025
Q&As: 120
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Which statement describes the correct use of pyspark.sql.functions.broadcast?
A. It marks a column as having low enough cardinality to properly map distinct values to available partitions, allowing a broadcast join.
B. It marks a column as small enough to store in memory on all executors, allowing a broadcast join.
C. It caches a copy of the indicated table on attached storage volumes for all active clusters within a Databricks workspace.
D. It marks a DataFrame as small enough to store in memory on all executors, allowing a broadcast join.
E. It caches a copy of the indicated table on all nodes in the cluster for use in all future queries during the cluster lifetime.
Correct Answer: D
https://spark.apache.org/docs/3.1.3/api/python/reference/api/pyspark.sql.functions.broadca st.html The broadcast function in PySpark is used in the context of joins. When you mark a DataFrame with broadcast, Spark tries to send this DataFrame to all worker nodes so that it can be joined with another DataFrame without shuffling the larger DataFrame across the nodes. This is particularly beneficial when the DataFrame is small enough to fit into the memory of each node. It helps to optimize the join process by reducing the amount of data that needs to be shuffled across the cluster, which can be a very expensive operation in terms of computation and time. The pyspark.sql.functions.broadcast function in PySpark is used to hint to Spark that a DataFrame is small enough to be broadcast to all worker nodes in the cluster. When this hint is applied, Spark can perform a broadcast join, where the smaller DataFrame is sent to each executor only once and joined with the larger DataFrame on each executor. This can significantly reduce the amount of data shuffled across the network and can improve the performance of the join operation. In a broadcast join, the entire smaller DataFrame is sent to each executor, not just a specific column or a cached version on attached storage. This function is particularly useful when one of the DataFrames in a join operation is much smaller than the other, and can fit comfortably in the memory of each executor node. References: Databricks Documentation on Broadcast Joins: Databricks Broadcast Join Guide PySpark API Reference: pyspark.sql.functions.broadcast
A Delta Lake table was created with the below query:
Consider the following query:
DROP TABLE prod.sales_by_store-
If this statement is executed by a workspace admin, which result will occur?
A. Nothing will occur until a COMMIT command is executed.
B. The table will be removed from the catalog but the data will remain in storage.
C. The table will be removed from the catalog and the data will be deleted.
D. An error will occur because Delta Lake prevents the deletion of production data.
E. Data will be marked as deleted but still recoverable with Time Travel.
Correct Answer: C
When a table is dropped in Delta Lake, the table is removed from the catalog and the data is deleted. This is because Delta Lake is a transactional storage layer that provides ACID guarantees. When a table is dropped, the transaction log is updated to reflect the deletion of the table and the data is deleted from the underlying storage. References: https://docs.databricks.com/delta/quick-start.html#drop-a-table https://docs.databricks.com/delta/delta-batch.html#drop-table
Which is a key benefit of an end-to-end test?
A. It closely simulates real world usage of your application.
B. It pinpoint errors in the building blocks of your application.
C. It provides testing coverage for all code paths and branches.
D. It makes it easier to automate your test suite
Correct Answer: A
End-to-end testing is a methodology used to test whether the flow of an application, from start to finish, behaves as expected. The key benefit of an end-to-end test is that it closely simulates real-world, user behavior, ensuring that the system
as a whole operates correctly.
References:
Software Testing: End-to-End Testing
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