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

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

2. Given the following code snippet in my_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 holds the DataFrame data and performs all computations locally
C) The driver node only provides the user interface for monitoring the application
D) The driver node orchestrates the execution by transforming actions into tasks and distributing them to worker nodes

3. 46 of 55.
A data engineer is implementing a streaming pipeline with watermarking to handle late-arriving records.
The engineer has written the following code:
inputStream \
.withWatermark("event_time", "10 minutes") \
.groupBy(window("event_time", "15 minutes"))
What happens to data that arrives after the watermark threshold?

A) Records that arrive later than the watermark threshold (10 minutes) will automatically be included in the aggregation if they fall within the 15-minute window.
B) Data arriving more than 10 minutes after the latest watermark will still be included in the aggregation but will be placed into the next window.
C) Any data arriving more than 10 minutes after the watermark threshold will be ignored and not included in the aggregation.
D) The watermark ensures that late data arriving within 10 minutes of the latest event time will be processed and included in the windowed aggregation.

4. 45 of 55.
Which feature of Spark Connect should be considered when designing an application that plans to enable remote interaction with a Spark cluster?

A) It is primarily used for data ingestion into Spark from external sources.
B) It provides a way to run Spark applications remotely in any programming language.
C) It can be used to interact with any remote cluster using the REST API.
D) It allows for remote execution of Spark jobs.

5. 13 of 55.
A developer needs to produce a Python dictionary using data stored in a small Parquet table, which looks like this:
region_id
region_name
10
North
12
East
14
West
The resulting Python dictionary must contain a mapping of region_id to region_name, containing the smallest 3 region_id values.
Which code fragment meets the requirements?

A) regions_dict = dict(regions.select("region_id", "region_name").rdd.collect())
B) regions_dict = dict(regions.take(3))
C) regions_dict = dict(regions.orderBy("region_id").limit(3).rdd.map(lambda x: (x.region_id, x.region_name)).collect())
D) regions_dict = regions.select("region_id", "region_name").take(3)

Solutions: