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

1. You have:
DataFrame A: 128 GB of transactions
DataFrame B: 1 GB user lookup table
Which strategy is correct for broadcasting?

A) DataFrame A should be broadcasted because it is larger and will eliminate the need for shuffling DataFrame B
B) DataFrame B should be broadcasted because it is smaller and will eliminate the need for shuffling DataFrame A
C) DataFrame B should be broadcasted because it is smaller and will eliminate the need for shuffling itself
D) DataFrame A should be broadcasted because it is smaller and will eliminate the need for shuffling itself

2. A Spark application suffers from too many small tasks due to excessive partitioning. How can this be fixed without a full shuffle?
Options:

A) Use the repartition() transformation with a lower number of partitions
B) Use the sortBy() transformation to reorganize the data
C) Use the coalesce() transformation with a lower number of partitions
D) Use the distinct() transformation to combine similar partitions

3. A data analyst builds a Spark application to analyze finance data and performs the following operations:filter, select,groupBy, andcoalesce.
Which operation results in a shuffle?

A) coalesce
B) groupBy
C) select
D) filter

4. A data engineer wants to write a Spark job that creates a new managed table. If the table already exists, the job should fail and not modify anything.
Which save mode and method should be used?

A) saveAsTable with mode ErrorIfExists
B) save with mode ErrorIfExists
C) saveAsTable with mode Overwrite
D) save with mode Ignore

5. Given the following code snippet inmy_spark_app.py:

What is the role of the driver node?

A) The driver node stores the final result after computations are completed by worker nodes
B) The driver node only provides the user interface for monitoring the application
C) The driver node orchestrates the execution by transforming actions into tasks and distributing them to worker nodes
D) The driver node holds the DataFrame data and performs all computations locally

Solutions: