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

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Databricks Associate-Developer-Apache-Spark-3.5 Exam Syllabus Topics:

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

1. A Spark engineer is troubleshooting a Spark application that has been encountering out-of-memory errors during execution. By reviewing the Spark driver logs, the engineer notices multiple "GC overhead limit exceeded" messages.
Which action should the engineer take to resolve this issue?

A) Modify the Spark configuration to disable garbage collection
B) Optimize the data processing logic by repartitioning the DataFrame.
C) Increase the memory allocated to the Spark Driver.
D) Cache large DataFrames to persist them in memory.

2. A developer is running Spark SQL queries and notices underutilization of resources. Executors are idle, and the number of tasks per stage is low.
What should the developer do to improve cluster utilization?

A) Increase the value of spark.sql.shuffle.partitions
B) Enable dynamic resource allocation to scale resources as needed
C) Increase the size of the dataset to create more partitions
D) Reduce the value of spark.sql.shuffle.partitions

3. 32 of 55.
A developer is creating a Spark application that performs multiple DataFrame transformations and actions. The developer wants to maintain optimal performance by properly managing the SparkSession.
How should the developer handle the SparkSession throughout the application?

A) Avoid using a SparkSession and rely on SparkContext only.
B) Use a single SparkSession instance for the entire application.
C) Create a new SparkSession instance before each transformation.
D) Stop and restart the SparkSession after each action.

4. A data scientist has identified that some records in the user profile table contain null values in any of the fields, and such records should be removed from the dataset before processing. The schema includes fields like user_id, username, date_of_birth, created_ts, etc.
The schema of the user profile table looks like this:

Which block of Spark code can be used to achieve this requirement?
Options:

A) filtered_df = users_raw_df.na.drop(how='any')
B) filtered_df = users_raw_df.na.drop(thresh=0)
C) filtered_df = users_raw_df.na.drop(how='all', thresh=None)
D) filtered_df = users_raw_df.na.drop(how='all')

5. A data analyst wants to add a column date derived from a timestamp column.
Options:

A) dates_df.withColumn("date", f.date_format("timestamp", "yyyy-MM-dd")).show()
B) dates_df.withColumn("date", f.to_date("timestamp")).show()
C) dates_df.withColumn("date", f.unix_timestamp("timestamp")).show()
D) dates_df.withColumn("date", f.from_unixtime("timestamp")).show()

Solutions: