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Databricks Certified Associate Developer for Apache Spark 3.5 - Python Sample Questions (Q47-Q52):

NEW QUESTION # 47
A data engineer observes that an upstream streaming source sends duplicate records, where duplicates share the same key and have at most a 30-minute difference inevent_timestamp. The engineer adds:
dropDuplicatesWithinWatermark("event_timestamp", "30 minutes")
What is the result?

A. It removes all duplicates regardless of when they arrive
B. It accepts watermarks in seconds and the code results in an error
C. It is not able to handle deduplication in this scenario
D. It removes duplicates that arrive within the 30-minute window specified by the watermark

Answer: D

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
The methoddropDuplicatesWithinWatermark()in Structured Streaming drops duplicate records based on a specified column and watermark window. The watermark defines the threshold for how late data is considered valid.
From the Spark documentation:
"dropDuplicatesWithinWatermark removes duplicates that occur within the event-time watermark window." In this case, Spark will retain the first occurrence and drop subsequent records within the 30-minute watermark window.
Final Answer: B

NEW QUESTION # 48
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", substring_index(col("email"), "@", 1))
.withColumn("domain", substring_index(col("email"), "@", -1))
C. customerDF.withColumn("username", split(col("email"), "@").getItem(0))
.withColumn("domain", split(col("email"), "@").getItem(1))
D. customerDF.select(
col("email").substr(0, 5).alias("username"),
col("email").substr(-5).alias("domain")
)

Answer: C

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 # 49
Given a CSV file with the content:

And the following code:
from pyspark.sql.types import *
schema = StructType([
StructField("name", StringType()),
StructField("age", IntegerType())
])
spark.read.schema(schema).csv(path).collect()
What is the resulting output?

A. [Row(name='alladin', age=20)]
B. [Row(name='bambi'), Row(name='alladin', age=20)]
C. [Row(name='bambi', age=None), Row(name='alladin', age=20)]
D. The code throws an error due to a schema mismatch.

Answer: C

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
In Spark, when a CSV row does not match the provided schema, Spark does not raise an error by default.
Instead, it returnsnullfor fields that cannot be parsed correctly.
In the first row,"hello"cannot be cast to Integer for theagefield # Spark setsage=None In the second row,"20"is a valid integer #age=20 So the output will be:
[Row(name='bambi', age=None), Row(name='alladin', age=20)]
Final Answer: C

NEW QUESTION # 50
A data engineer is working on a real-time analytics pipeline using Apache Spark Structured Streaming. The engineer wants to process incoming data and ensure that triggers control when the query is executed. The system needs to process data in micro-batches with a fixed interval of 5 seconds.
Which code snippet the data engineer could use to fulfil this requirement?
A)

B)

C)

D)

Options:

A. Uses trigger(processingTime='5 seconds') - correct micro-batch trigger with interval.
B. Uses trigger() - default micro-batch trigger without interval.
C. Uses trigger(processingTime=5000) - invalid, as processingTime expects a string.
D. Uses trigger(continuous='5 seconds') - continuous processing mode.

Answer: A

Explanation:
To define a micro-batch interval, the correct syntax is:
query = df.writeStream
outputMode("append")
trigger(processingTime='5 seconds')
start()
This schedules the query to execute every 5 seconds.
Continuous mode (used in Option A) is experimental and has limited sink support.
Option D is incorrect because processingTime must be a string (not an integer).
Option B triggers as fast as possible without interval control.
Reference:Spark Structured Streaming - Triggers

NEW QUESTION # 51
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='all', thresh=None)
C. filtered_df = users_raw_df.na.drop(thresh=0)
D. filtered_df = users_raw_df.na.drop(how='any')

Answer: D

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 # 52
......

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Databricks Certified Associate Developer for Apache Spark 3.5 - Python Sample Questions (Q47-Q52):

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