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

1. Given the following code snippet in my_spark_app.py:

What is the role of the driver node?

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

2. A Spark engineer must select an appropriate deployment mode for the Spark jobs.
What is the benefit of using cluster mode in Apache Spark™?

A) In cluster mode, the driver is responsible for executing all tasks locally without distributing them across the worker nodes.
B) In cluster mode, the driver program runs on one of the worker nodes, allowing the application to fully utilize the distributed resources of the cluster.
C) In cluster mode, the driver runs on the client machine, which can limit the application's ability to handle large datasets efficiently.
D) In cluster mode, resources are allocated from a resource manager on the cluster, enabling better performance and scalability for large jobs

3. 21 of 55.
What is the behavior of the function date_sub(start, days) if a negative value is passed into the days parameter?

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

4. 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) The number of days specified will be removed from the start date
C) An error message of an invalid parameter will be returned
D) The same start date will be returned

5. 47 of 55.
A data engineer has written the following code to join two DataFrames df1 and df2:
df1 = spark.read.csv("sales_data.csv")
df2 = spark.read.csv("product_data.csv")
df_joined = df1.join(df2, df1.product_id == df2.product_id)
The DataFrame df1 contains ~10 GB of sales data, and df2 contains ~8 MB of product data.
Which join strategy will Spark use?

A) Shuffle join, because AQE is not enabled, and Spark uses a static query plan.
B) Shuffle join, as the size difference between df1 and df2 is too large for a broadcast join to work efficiently.
C) Shuffle join because no broadcast hints were provided.
D) Broadcast join, as df2 is smaller than the default broadcast threshold.

Solutions: