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

1. A data engineer is working on the DataFrame:

(Referring to the table image: it has columns Id, Name, count, and timestamp.) Which code fragment should the engineer use to extract the unique values in the Name column into an alphabetically ordered list?

A) df.select("Name").orderBy(df["Name"].asc())
B) df.select("Name").distinct().orderBy(df["Name"].desc())
C) df.select("Name").distinct()
D) df.select("Name").distinct().orderBy(df["Name"])

2. 30 of 55.
A data engineer is working on a num_df DataFrame and has a Python UDF defined as:
def cube_func(val):
return val * val * val
Which code fragment registers and uses this UDF as a Spark SQL function to work with the DataFrame num_df?

A) num_df.register("cube_func").select("num").show()
B) spark.udf.register("cube_func", cube_func)
num_df.selectExpr("cube_func(num)").show()
C) num_df.select(cube_func("num")).show()
D) spark.createDataFrame(cube_func("num")).show()

3. 4 of 55.
A developer is working on a Spark application that processes a large dataset using SQL queries. Despite having a large cluster, the developer notices that the job is underutilizing the available resources. Executors remain idle for most of the time, and logs reveal that the number of tasks per stage is very low. The developer suspects that this is causing suboptimal cluster performance.
Which action should the developer take to improve cluster utilization?

A) Increase the value of spark.sql.shuffle.partitions
B) Enable dynamic resource allocation to scale resources as needed
C) Reduce the value of spark.sql.shuffle.partitions
D) Increase the size of the dataset to create more partitions

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

5. A Spark engineer is troubleshooting a Spark application that has been encountering out-of-memory errors during execution. By reviewing the Spark driver logs, the engineer notices multiple "GC overhead limit exceeded" messages.
Which action should the engineer take to resolve this issue?

A) Optimize the data processing logic by repartitioning the DataFrame.
B) Modify the Spark configuration to disable garbage collection
C) Cache large DataFrames to persist them in memory.
D) Increase the memory allocated to the Spark Driver.

Solutions: