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

1. 29 of 55.
A Spark application is experiencing performance issues in client mode due to the driver being resource-constrained.
How should this issue be resolved?

A) Add more executor instances to the cluster.
B) Increase the driver memory on the client machine.
C) Switch the deployment mode to cluster mode.
D) Switch the deployment mode to local mode.

2. You have:
DataFrame A: 128 GB of transactions
DataFrame B: 1 GB user lookup table
Which strategy is correct for broadcasting?

A) DataFrame A should be broadcasted because it is smaller and will eliminate the need for shuffling itself
B) DataFrame A should be broadcasted because it is larger and will eliminate the need for shuffling DataFrame B
C) DataFrame B should be broadcasted because it is smaller and will eliminate the need for shuffling itself
D) DataFrame B should be broadcasted because it is smaller and will eliminate the need for shuffling DataFrame A

3. A data engineer observes that an upstream streaming source sends duplicate records, where duplicates share the same key and have at most a 30-minute difference in event_timestamp. The engineer adds:
dropDuplicatesWithinWatermark("event_timestamp", "30 minutes")
What is the result?

A) It removes duplicates that arrive within the 30-minute window specified by the watermark
B) It accepts watermarks in seconds and the code results in an error
C) It is not able to handle deduplication in this scenario
D) It removes all duplicates regardless of when they arrive

4. A data engineer needs to write a Streaming DataFrame as Parquet files.
Given the code:

Which code fragment should be inserted to meet the requirement?
A)

B)

C)

D)

Which code fragment should be inserted to meet the requirement?

A) .format("parquet")
.option("location", "path/to/destination/dir")
B) CopyEdit
.option("format", "parquet")
.option("destination", "path/to/destination/dir")
C) .format("parquet")
.option("path", "path/to/destination/dir")
D) .option("format", "parquet")
.option("location", "path/to/destination/dir")

5. A data scientist wants each record in the DataFrame to contain:
The first attempt at the code does read the text files but each record contains a single line. This code is shown below:

The entire contents of a file
The full file path
The issue: reading line-by-line rather than full text per file.
Code:
corpus = spark.read.text("/datasets/raw_txt/*") \
.select('*', '_metadata.file_path')
Which change will ensure one record per file?
Options:

A) Add the option lineSep=", " to the text() function
B) Add the option wholetext=True to the text() function
C) Add the option lineSep='\n' to the text() function
D) Add the option wholetext=False to the text() function

Solutions: