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

1. 1 of 55. A data scientist wants to ingest a directory full of plain text files so that each record in the output DataFrame contains the entire contents of a single file and the full path of the file the text was read from.
The first attempt does read the text files, but each record contains a single line. This code is shown below:
txt_path = "/datasets/raw_txt/*"
df = spark.read.text(txt_path) # one row per line by default
df = df.withColumn("file_path", input_file_name()) # add full path
Which code change can be implemented in a DataFrame that meets the data scientist's requirements?

A) Add the option wholetext to the text() function.
B) Add the option lineSep to the text() function.
C) Add the option wholetext=False to the text() function.
D) Add the option lineSep=", " to the text() function.

2. 15 of 55.
A data engineer is working on a Streaming DataFrame (streaming_df) with the following streaming data:
id
name
count
timestamp
1
Delhi
20
2024-09-19T10:11
1
Delhi
50
2024-09-19T10:12
2
London
50
2024-09-19T10:15
3
Paris
30
2024-09-19T10:18
3
Paris
20
2024-09-19T10:20
4
Washington
10
2024-09-19T10:22
Which operation is supported with streaming_df?

A) streaming_df.filter("count < 30")
B) streaming_df.select(countDistinct("name"))
C) streaming_df.count()
D) streaming_df.show()

3. A Spark application developer wants to identify which operations cause shuffling, leading to a new stage in the Spark execution plan.
Which operation results in a shuffle and a new stage?

A) DataFrame.filter()
B) DataFrame.groupBy().agg()
C) DataFrame.select()
D) DataFrame.withColumn()

4. Which Spark configuration controls the number of tasks that can run in parallel on the executor?
Options:

A) spark.driver.cores
B) spark.executor.cores
C) spark.task.maxFailures
D) spark.executor.memory

5. An engineer has a large ORC file located at /file/test_data.orc and wants to read only specific columns to reduce memory usage.
Which code fragment will select the columns, i.e., col1, col2, during the reading process?

A) spark.read.orc("/file/test_data.orc").filter("col1 = 'value' ").select("col2")
B) spark.read.orc("/file/test_data.orc").selected("col1", "col2")
C) spark.read.format("orc").load("/file/test_data.orc").select("col1", "col2")
D) spark.read.format("orc").select("col1", "col2").load("/file/test_data.orc")

Solutions: