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

1. 37 of 55.
A data scientist is working with a Spark DataFrame called customerDF that contains customer information.
The DataFrame has a column named email with customer email addresses.
The data scientist needs to split this column into username and domain parts.
Which code snippet splits the email column into username and domain columns?

A) customerDF = customerDF.withColumn("domain", col("email").split("@")[1])
B) customerDF = customerDF \
.withColumn("username", split(col("email"), "@").getItem(0)) \
.withColumn("domain", split(col("email"), "@").getItem(1))
C) customerDF = customerDF.select("email").alias("username", "domain")
D) customerDF = customerDF.withColumn("username", regexp_replace(col("email"), "@", ""))

2. 49 of 55.
In the code block below, aggDF contains aggregations on a streaming DataFrame:
aggDF.writeStream \
.format("console") \
.outputMode("???") \
.start()
Which output mode at line 3 ensures that the entire result table is written to the console during each trigger execution?

A) AGGREGATE
B) COMPLETE
C) APPEND
D) REPLACE

3. 43 of 55.
An organization has been running a Spark application in production and is considering disabling the Spark History Server to reduce resource usage.
What will be the impact of disabling the Spark History Server in production?

A) Prevention of driver log accumulation during long-running jobs
B) Enhanced executor performance due to reduced log size
C) Improved job execution speed due to reduced logging overhead
D) Loss of access to past job logs and reduced debugging capability for completed jobs

4. What is the risk associated with this operation when converting a large Pandas API on Spark DataFrame back to a Pandas DataFrame?

A) The conversion will automatically distribute the data across worker nodes
B) The operation will fail if the Pandas DataFrame exceeds 1000 rows
C) Data will be lost during conversion
D) The operation will load all data into the driver's memory, potentially causing memory overflow

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

Solutions: