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Databricks Certified Associate Developer for Apache Spark 3.5 - Python Sample Questions:

1. What is the benefit of Adaptive Query Execution (AQE)?

A) It allows Spark to optimize the query plan before execution but does not adapt during runtime.
B) It optimizes query execution by parallelizing tasks and does not adjust strategies based on runtime metrics like data skew.
C) It automatically distributes tasks across nodes in the clusters and does not perform runtime adjustments to the query plan.
D) It enables the adjustment of the query plan during runtime, handling skewed data, optimizing join strategies, and improving overall query performance.

2. What is the behavior for function date_sub(start, days) if a negative value is passed into the days parameter?

A) The number of days specified will be added to the start date
B) An error message of an invalid parameter will be returned
C) The number of days specified will be removed from the start date
D) The same start date will be returned

3. 30 of 55.
A data engineer is working on a num_df DataFrame and has a Python UDF defined as:
def cube_func(val):
return val * val * val
Which code fragment registers and uses this UDF as a Spark SQL function to work with the DataFrame num_df?

A) spark.createDataFrame(cube_func("num")).show()
B) spark.udf.register("cube_func", cube_func)
num_df.selectExpr("cube_func(num)").show()
C) num_df.register("cube_func").select("num").show()
D) num_df.select(cube_func("num")).show()

4. Which feature of Spark Connect is considered when designing an application to enable remote interaction with the Spark cluster?

A) It is primarily used for data ingestion into Spark from external sources
B) It provides a way to run Spark applications remotely in any programming language
C) It allows for remote execution of Spark jobs
D) It can be used to interact with any remote cluster using the REST API

5. An application architect has been investigating Spark Connect as a way to modernize existing Spark applications running in their organization.
Which requirement blocks the adoption of Spark Connect in this organization?

A) Stability: isolation of application code and dependencies from each other and the Spark driver
B) Upgradability: the ability to upgrade the Spark applications independently from the Spark driver itself
C) Debuggability: the ability to perform interactive debugging directly from the application code
D) Complete Spark API support: the ability to migrate all existing code to Spark Connect without modification, including the RDD APIs

Solutions: