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

1. A developer wants to test Spark Connect with an existing Spark application.
What are the two alternative ways the developer can start a local Spark Connect server without changing their existing application code? (Choose 2 answers)

A) Add .remote("sc://localhost") to their SparkSession.builder calls in their Spark code
B) Set the environment variable SPARK_REMOTE="sc://localhost" before starting the pyspark shell
C) Ensure the Spark property spark.connect.grpc.binding.port is set to 15002 in the application code
D) Execute their pyspark shell with the option --remote "https://localhost"
E) Execute their pyspark shell with the option --remote "sc://localhost"

2. A data engineer is working on a Streaming DataFrame streaming_df with the given streaming data:

Which operation is supported with streamingdf ?

A) streaming_df.groupby("Id") .count ()
B) streaming_df.filter (col("count") < 30).show()
C) streaming_df.orderBy("timestamp").limit(4)
D) streaming_df. select (countDistinct ("Name") )

3. A data engineer is working on a real-time analytics pipeline using Apache Spark Structured Streaming. The engineer wants to process incoming data and ensure that triggers control when the query is executed. The system needs to process data in micro-batches with a fixed interval of 5 seconds.
Which code snippet the data engineer could use to fulfil this requirement?
A)

B)

C)

D)

Options:

A) Uses trigger() - default micro-batch trigger without interval.
B) Uses trigger(processingTime='5 seconds') - correct micro-batch trigger with interval.
C) Uses trigger(processingTime=5000) - invalid, as processingTime expects a string.
D) Uses trigger(continuous='5 seconds') - continuous processing mode.

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

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

5. A data engineer wants to create a Streaming DataFrame that reads from a Kafka topic called feed.

Which code fragment should be inserted in line 5 to meet the requirement?
Code context:
spark \
.readStream \
.format("kafka") \
.option("kafka.bootstrap.servers", "host1:port1,host2:port2") \
.[LINE 5] \
.load()
Options:

A) .option("subscribe.topic", "feed")
B) .option("subscribe", "feed")
C) .option("topic", "feed")
D) .option("kafka.topic", "feed")

Solutions: