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Databricks Certified Associate Developer for Apache Spark 3.5 - Python Sample Questions (Q101-Q106):

NEW QUESTION # 101
A data scientist has identified that some records in the user profile table contain null values in any of the fields, and such records should be removed from the dataset before processing. The schema includes fields like user_id, username, date_of_birth, created_ts, etc.
The schema of the user profile table looks like this:

Which block of Spark code can be used to achieve this requirement?
Options:

A. filtered_df = users_raw_df.na.drop(how='all')
B. filtered_df = users_raw_df.na.drop(how='any')
C. filtered_df = users_raw_df.na.drop(how='all', thresh=None)
D. filtered_df = users_raw_df.na.drop(thresh=0)

Answer: B

Explanation:
na.drop(how='any')drops any row that has at least one null value.
This is exactly what's needed when the goal is to retain only fully complete records.
Usage:CopyEdit
filtered_df = users_raw_df.na.drop(how='any')
Explanation of incorrect options:
A: thresh=0 is invalid - thresh must be # 1.
B: how='all' drops only rows where all columns are null (too lenient).
D: spark.na.drop doesn't support mixing how and thresh in that way; it's incorrect syntax.
Reference:PySpark DataFrameNaFunctions.drop()

NEW QUESTION # 102
40 of 55.
A developer wants to refactor older Spark code to take advantage of built-in functions introduced in Spark 3.5.
The original code:
from pyspark.sql import functions as F
min_price = 110.50
result_df = prices_df.filter(F.col("price") > min_price).agg(F.count("*")) Which code block should the developer use to refactor the code?

A. result_df = prices_df.withColumn("valid_price", when(col("price") > F.lit(min_price), True))
B. result_df = prices_df.filter(F.col("price") > F.lit(min_price)).agg(F.count("*"))
C. result_df = prices_df.where(F.lit("price") > min_price).groupBy().count()
D. result_df = prices_df.filter(F.lit(min_price) > F.col("price")).count()

Answer: B

Explanation:
To compare a column value with a Python literal constant in a DataFrame expression, use F.lit() to convert it into a Spark literal.
Correct refactor:
from pyspark.sql import functions as F
min_price = 110.50
result_df = prices_df.filter(F.col("price") > F.lit(min_price)).agg(F.count("*")) This avoids type mismatches and ensures Spark executes the filter expression on the cluster.
Why the other options are incorrect:
B: where() syntax is valid, but F.lit("price") is incorrect - wraps string literal, not a column.
C: withColumn adds a column, not needed for this aggregation.
D: Comparison logic reversed.
Reference:
PySpark SQL Functions - lit(), col(), and DataFrame filters.
Databricks Exam Guide (June 2025): Section "Developing Apache Spark DataFrame/DataSet API Applications" - filtering, literals, and aggregations.

NEW QUESTION # 103
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_pandas()
C. psdf.to_pyspark()
D. psdf.to_spark()

Answer: D

Explanation:
Pandas API on Spark (pyspark.pandas) allows interoperability with PySpark DataFrames. To convert a pyspark.pandas.DataFrame to 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 # 104
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("all")
B. filtered_users = raw_users.dropna(how="all")
C. filtered_users = raw_users.na.drop("any")
D. filtered_users = raw_users.dropna(how="any")

Answer: D

Explanation:
In Spark's DataFrame API, the dropna() (or equivalently, DataFrameNaFunctions.drop()) method removes rows containing null values.
Behavior:
how="any" → drops rows where any column has a null value.
how="all" → drops rows where all columns are null.
Since the data scientist wants to drop records with any null field, the correct parameter is how="any".
Correct syntax:
filtered_users = raw_users.dropna(how="any")
This will remove all records that have at least one null value in any column.
Why the other options are incorrect:
A: Uses na.drop("any") but missing parentheses context (works only as raw_users.na.drop("any"), which is equivalent to option C).
B/D: how="all" only removes rows where all values are null - too strict for this use case.
Reference:
PySpark DataFrame API - DataFrameNaFunctions.drop() and DataFrame.dropna().
Databricks Exam Guide (June 2025): Section "Developing Apache Spark DataFrame/DataSet API Applications" - covers handling missing data and DataFrame cleaning operations.

NEW QUESTION # 105
A data scientist is working on a project that requires processing large amounts of structured data, performing SQL queries, and applying machine learning algorithms. The data scientist is considering using Apache Spark for this task.
Which combination of Apache Spark modules should the data scientist use in this scenario?
Options:

A. Spark SQL, Pandas API on Spark, and Structured Streaming
B. Spark DataFrames, Structured Streaming, and GraphX
C. Spark DataFrames, Spark SQL, and MLlib
D. Spark Streaming, GraphX, and Pandas API on Spark

Answer: C

Explanation:
Comprehensive Explanation:
To cover structured data processing, SQL querying, and machine learning in Apache Spark, the correct combination of components is:
Spark DataFrames: for structured data processing
Spark SQL: to execute SQL queries over structured data
MLlib: Spark's scalable machine learning library
This trio is designed for exactly this type of use case.
Why other options are incorrect:
A: GraphX is for graph processing - not needed here.
B: Pandas API on Spark is useful, but MLlib is essential for ML, which this option omits.
C: Spark Streaming is legacy; GraphX is irrelevant here.
Reference:Apache Spark Modules Overview

NEW QUESTION # 106
......

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