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Databricks Certified Associate Developer for Apache Spark 3.5 - Python Sample Questions (Q73-Q78):

NEW QUESTION # 73
A data scientist of an e-commerce company is working with user data obtained from its subscriber database and has stored the data in a DataFrame df_user. Before further processing the data, the data scientist wants to create another DataFrame df_user_non_pii and store only the non-PII columns in this DataFrame. The PII columns in df_user are first_name, last_name, email, and birthdate.
Which code snippet can be used to meet this requirement?

A. df_user_non_pii = df_user.drop("first_name", "last_name", "email", "birthdate")
B. df_user_non_pii = df_user.dropfields("first_name", "last_name", "email", "birthdate")
C. df_user_non_pii = df_user.drop("first_name", "last_name", "email", "birthdate")
D. df_user_non_pii = df_user.dropfields("first_name, last_name, email, birthdate")

Answer: C

Explanation:
Comprehensive and Detailed Explanation:
To remove specific columns from a PySpark DataFrame, the drop() method is used. This method returns a new DataFrame without the specified columns. The correct syntax for dropping multiple columns is to pass each column name as a separate argument to the drop() method.
Correct Usage:
df_user_non_pii = df_user.drop("first_name", "last_name", "email", "birthdate") This line of code will return a new DataFrame df_user_non_pii that excludes the specified PII columns.
Explanation of Options:
A).Correct. Uses the drop() method with multiple column names passed as separate arguments, which is the standard and correct usage in PySpark.
B).Although it appears similar to Option A, if the column names are not enclosed in quotes or if there's a syntax error (e.g., missing quotes or incorrect variable names), it would result in an error. However, as written, it's identical to Option A and thus also correct.
C).Incorrect. The dropfields() method is not a method of the DataFrame class in PySpark. It's used with StructType columns to drop fields from nested structures, not top-level DataFrame columns.
D).Incorrect. Passing a single string with comma-separated column names to dropfields() is not valid syntax in PySpark.
References:
PySpark Documentation:DataFrame.drop
Stack Overflow Discussion:How to delete columns in PySpark DataFrame

NEW QUESTION # 74
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 lineSep to the text() function.
B. Add the option wholetext to the text() function.
C. Add the option wholetext=False to the text() function.
D. Add the option lineSep=", " to the text() function.

Answer: B

Explanation:
By default, the spark.read.text() method reads a text file one line per record. This means that each line in a text file becomes one row in the resulting DataFrame.
To read each file as a single record, Apache Spark provides the option wholetext, which, when set to True, causes Spark to treat the entire file contents as one single string per row.
Correct usage:
df = spark.read.option("wholetext", True).text(txt_path)
This way, each record in the DataFrame will contain the full content of one file instead of one line per record.
To also include the file path, the function input_file_name() can be used to create an additional column that stores the complete path of the file being read:
from pyspark.sql.functions import input_file_name
df = spark.read.option("wholetext", True).text(txt_path)
.withColumn("file_path", input_file_name())
This approach satisfies both requirements from the question:
Each record holds the entire contents of a file.
Each record also contains the file path from which the text was read.
Why the other options are incorrect:
B or D (lineSep) - The lineSep option only defines the delimiter between lines. It does not combine the entire file content into a single record.
C (wholetext=False) - This is the default behavior, which still reads one record per line rather than per file.
Reference (Databricks Apache Spark 3.5 - Python / Study Guide):
PySpark API Reference: DataFrameReader.text - describes the wholetext option.
PySpark Functions: input_file_name() - adds a column with the source file path.
Databricks Certified Associate Developer for Apache Spark Exam Guide (June 2025): Section "Using Spark DataFrame APIs" - covers reading files and handling DataFrames.

NEW QUESTION # 75
A data engineer is streaming data from Kafka and requires:
Minimal latency
Exactly-once processing guarantees
Which trigger mode should be used?

A. .trigger(continuous=True)
B. .trigger(availableNow=True)
C. .trigger(continuous='1 second')
D. .trigger(processingTime='1 second')

Answer: D

Explanation:
Comprehensive and Detailed Explanation:
Exactly-once guarantees in Spark Structured Streaming require micro-batch mode (default), not continuous mode.
Continuous mode (.trigger(continuous=...)) only supports at-least-once semantics and lacks full fault- tolerance.
trigger(availableNow=True)is a batch-style trigger, not suited for low-latency streaming.
So:
Option A uses micro-batching with a tight trigger interval # minimal latency + exactly-once guarantee.
Final Answer: A

NEW QUESTION # 76
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. Broadcast join, as df2 is smaller than the default broadcast threshold
B. Shuffle join because no broadcast hints were provided
C. Shuffle join, as the size difference between df1 and df2 is too large for a broadcast join to work efficiently
D. Shuffle join, because AQE is not enabled, and Spark uses a static query plan

Answer: A

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 # 77
26 of 55.
A data scientist at an e-commerce company is working with user data obtained from its subscriber database and has stored the data in a DataFrame df_user.
Before further processing, the data scientist wants to create another DataFrame df_user_non_pii and store only the non-PII columns.
The PII columns in df_user are name, email, and birthdate.
Which code snippet can be used to meet this requirement?

A. df_user_non_pii = df_user.remove("name", "email", "birthdate")
B. df_user_non_pii = df_user.select("name", "email", "birthdate")
C. df_user_non_pii = df_user.drop("name", "email", "birthdate")
D. df_user_non_pii = df_user.dropFields("name", "email", "birthdate")

Answer: C

Explanation:
To exclude sensitive (PII) columns from a DataFrame, the easiest method is to use the .drop() function with the list of column names to remove.
Correct syntax:
df_user_non_pii = df_user.drop("name", "email", "birthdate")
This creates a new DataFrame containing all remaining columns.
Why the other options are incorrect:
B: .dropFields() is not valid for standard DataFrames - it's used for struct fields only.
C: .select() would keep only PII columns, not remove them.
D: .remove() does not exist in Spark DataFrame API.
Reference:
PySpark DataFrame API - drop() method for removing multiple columns.
Databricks Exam Guide (June 2025): Section "Developing Apache Spark DataFrame/DataSet API Applications" - data manipulation, selecting, and dropping columns.

NEW QUESTION # 78
......

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