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Databricks Certified Associate Developer for Apache Spark 3.5 - Python Sample Questions (Q114-Q119):

NEW QUESTION # 114
Given this view definition:
df.createOrReplaceTempView("users_vw")
Which approach can be used to query the users_vw view after the session is terminated?
Options:

A. Query the users_vw using Spark
B. Recreate the users_vw and query the data using Spark
C. Persist the users_vw data as a table
D. Save the users_vw definition and query using Spark

Answer: C

Explanation:
Temp views like createOrReplaceTempView are session-scoped.
They disappear once the Spark session ends.
To retain data across sessions, it must be persisted:
df.write.saveAsTable("users_vw")
Thus, the view needs to be persisted as a table to survive session termination.

NEW QUESTION # 115
Given the code fragment:

import pyspark.pandas as ps
psdf = ps.DataFrame({'col1': [1, 2], 'col2': [3, 4]})
Which method is used to convert a Pandas API on Spark DataFrame (pyspark.pandas.DataFrame) into a standard PySpark DataFrame (pyspark.sql.DataFrame)?

A. psdf.to_dataframe()
B. psdf.to_spark()
C. psdf.to_pandas()
D. psdf.to_pyspark()

Answer: B

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
Pandas API on Spark (pyspark.pandas) allows interoperability with PySpark DataFrames. To convert apyspark.pandas.DataFrameto a standard PySpark DataFrame, you use.to_spark().
Example:
df = psdf.to_spark()
This is the officially supported method as per Databricks Documentation.
Incorrect options:
B, D: Invalid or nonexistent methods.
C: Converts to a local pandas DataFrame, not a PySpark DataFrame.

NEW QUESTION # 116
A data engineer writes the following code to join two DataFrames df1 and df2:
df1 = spark.read.csv("sales_data.csv") # ~10 GB
df2 = spark.read.csv("product_data.csv") # ~8 MB
result = df1.join(df2, df1.product_id == df2.product_id)

Which join strategy will Spark use?

A. Shuffle join, as the size difference between df1 and df2 is too large for a broadcast join to work efficiently
B. Shuffle join, because AQE is not enabled, and Spark uses a static query plan
C. Shuffle join because no broadcast hints were provided
D. Broadcast join, as df2 is smaller than the default broadcast threshold

Answer: D

Explanation:
The default broadcast join threshold in Spark is:
spark.sql.autoBroadcastJoinThreshold = 10MB
Since df2 is only 8 MB (less than 10 MB), Spark will automatically apply a broadcast join without requiring explicit hints.
From the Spark documentation:
"If one side of the join is smaller than the broadcast threshold, Spark will automatically broadcast it to all executors." A is incorrect because Spark does support auto broadcast even with static plans.
B is correct: Spark will automatically broadcast df2.
C and D are incorrect because Spark's default logic handles this optimization.
Final answer: B

NEW QUESTION # 117
A data scientist is working with a Spark DataFrame called customerDF that contains customer information.
The DataFrame has a column named email with customer email addresses. The data scientist needs to split this column into username and domain parts.
Which code snippet splits the email column into username and domain columns?

A. customerDF.select(
regexp_replace(col("email"), "@", "").alias("username"),
regexp_replace(col("email"), "@", "").alias("domain")
)
B. customerDF.withColumn("username", split(col("email"), "@").getItem(0))
.withColumn("domain", split(col("email"), "@").getItem(1))
C. customerDF.withColumn("username", substring_index(col("email"), "@", 1))
.withColumn("domain", substring_index(col("email"), "@", -1))
D. customerDF.select(
col("email").substr(0, 5).alias("username"),
col("email").substr(-5).alias("domain")
)

Answer: B

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
Option B is the correct and idiomatic approach in PySpark to split a string column (like email) based on a delimiter such as "@".
The split(col("email"), "@") function returns an array with two elements: username and domain.
getItem(0) retrieves the first part (username).
getItem(1) retrieves the second part (domain).
withColumn() is used to create new columns from the extracted values.
Example from official Databricks Spark documentation on splitting columns:
from pyspark.sql.functions import split, col
df.withColumn("username", split(col("email"), "@").getItem(0))
withColumn("domain", split(col("email"), "@").getItem(1))
##Why other options are incorrect:
A uses fixed substring indices (substr(0, 5)), which won't correctly extract usernames and domains of varying lengths.
C uses substring_index, which is available but less idiomatic for splitting emails and is slightly less readable.
D removes "@" from the email entirely, losing the separation between username and domain, and ends up duplicating values in both fields.
Therefore, Option B is the most accurate and reliable solution according to Apache Spark 3.5 best practices.

NEW QUESTION # 118
10 of 55.
What is the benefit of using Pandas API on Spark for data transformations?

A. It runs on a single node only, utilizing memory efficiently.
B. It is available only with Python, thereby reducing the learning curve.
C. It computes results immediately using eager execution.
D. It executes queries faster using all the available cores in the cluster as well as provides Pandas's rich set of features.

Answer: D

Explanation:
Pandas API on Spark provides a distributed implementation of the Pandas DataFrame API on top of Apache Spark.
Advantages:
Executes transformations in parallel across all nodes and cores in the cluster.
Maintains Pandas-like syntax, making it easy for Python users to transition.
Enables scaling of existing Pandas code to handle large datasets without memory limits.
Therefore, it combines Pandas usability with Spark's distributed power, offering both speed and scalability.
Why the other options are incorrect:
B: While it uses Python, that's not its main advantage.
C: It runs distributed across the cluster, not on a single node.
D: Pandas API on Spark uses lazy evaluation, not eager computation.
Reference:
PySpark Pandas API Overview - advantages of distributed execution.
Databricks Exam Guide (June 2025): Section "Using Pandas API on Apache Spark" - explains the benefits of Pandas API integration for scalable transformations.

NEW QUESTION # 119
......

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