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Databricks Associate-Developer-Apache-Spark-3.5 Exam Syllabus Topics:

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

Question 1

48 of 55.
A data engineer needs to join multiple DataFrames and has written the following code:
from pyspark.sql.functions import broadcast
data1 = [(1, "A"), (2, "B")]
data2 = [(1, "X"), (2, "Y")]
data3 = [(1, "M"), (2, "N")]
df1 = spark.createDataFrame(data1, ["id", "val1"])
df2 = spark.createDataFrame(data2, ["id", "val2"])
df3 = spark.createDataFrame(data3, ["id", "val3"])
df_joined = df1.join(broadcast(df2), "id", "inner") \
.join(broadcast(df3), "id", "inner")
What will be the output of this code?

A. The code will fail because only one broadcast join can be performed at a time.
B. The code will result in an error because broadcast() must be called before the joins, not inline.
C. The code will fail because the second join condition (df2.id == df3.id) is incorrect.
D. The code will work correctly and perform two broadcast joins simultaneously to join df1 with df2, and then the result with df3.

Question 2

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

Question 3

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

Question 4

12 of 55.
A data scientist has been investigating user profile data to build features for their model. After some exploratory data analysis, the data scientist identified that some records in the user profiles contain NULL values in too many fields to be useful.
The schema of the user profile table looks like this:
user_id STRING,
username STRING,
date_of_birth DATE,
country STRING,
created_at TIMESTAMP
The data scientist decided that if any record contains a NULL value in any field, they want to remove that record from the output before further processing.
Which block of Spark code can be used to achieve these requirements?

A. filtered_users = raw_users.na.drop("any")
B. filtered_users = raw_users.na.drop("all")
C. filtered_users = raw_users.dropna(how="any")
D. filtered_users = raw_users.dropna(how="all")

Question 5

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

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

Solutions: