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Databricks Certified Associate Developer for Apache Spark 3.5 - Python Sample Questions (Q116-Q121):

NEW QUESTION # 116
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.dropfields("first_name, last_name, email, birthdate")
D. df_user_non_pii = df_user.drop("first_name", "last_name", "email", "birthdate")

Answer: D

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 # 117
35 of 55.
A data engineer is building a Structured Streaming pipeline and wants it to recover from failures or intentional shutdowns by continuing where it left off.
How can this be achieved?

A. By configuring the option checkpointLocation during readStream.
B. By configuring the option recoveryLocation during writeStream.
C. By configuring the option checkpointLocation during writeStream.
D. By configuring the option recoveryLocation during SparkSession initialization.

Answer: C

Explanation:
In Structured Streaming, checkpoints store state information (offsets, progress, and metadata) needed to resume a stream after a failure or restart.
Correct usage:
Set the checkpointLocation option when writing the streaming output:
streaming_df.writeStream
.format("delta")
.option("checkpointLocation", "/path/to/checkpoint/dir")
.start("/path/to/output")
Spark uses this checkpoint directory to recover progress automatically and maintain exactly-once semantics.
Why the other options are incorrect:
A/D: recoveryLocation is not a valid Spark configuration option.
B: Checkpointing must be configured in writeStream, not during readStream.
Reference:
PySpark Structured Streaming Guide - Checkpointing and recovery.
Databricks Exam Guide (June 2025): Section "Structured Streaming" - explains checkpointing and fault-tolerant streaming recovery.

NEW QUESTION # 118
A Spark DataFramedfis cached using theMEMORY_AND_DISKstorage level, but the DataFrame is too large to fit entirely in memory.
What is the likely behavior when Spark runs out of memory to store the DataFrame?

A. Spark duplicates the DataFrame in both memory and disk. If it doesn't fit in memory, the DataFrame is stored and retrieved from the disk entirely.
B. Spark splits the DataFrame evenly between memory and disk, ensuring balanced storage utilization.
C. Spark stores the frequently accessed rows in memory and less frequently accessed rows on disk, utilizing both resources to offer balanced performance.
D. Spark will store as much data as possible in memory and spill the rest to disk when memory is full, continuing processing with performance overhead.

Answer: D

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
When using theMEMORY_AND_DISKstorage level, Spark attempts to cache as much of the DataFrame in memory as possible. If the DataFrame does not fit entirely in memory, Spark will store the remaining partitions on disk. This allows processing to continue, albeit with a performance overhead due to disk I/O.
As per the Spark documentation:
"MEMORY_AND_DISK: It stores partitions that do not fit in memory on disk and keeps the rest in memory.
This can be useful when working with datasets that are larger than the available memory."
- Perficient Blogs: Spark - StorageLevel
This behavior ensures that Spark can handle datasets larger than the available memory by spilling excess data to disk, thus preventing job failures due to memory constraints.

NEW QUESTION # 119
What is the relationship between jobs, stages, and tasks during execution in Apache Spark?
Options:

A. A stage contains multiple tasks, and each task contains multiple jobs.
B. A stage contains multiple jobs, and each job contains multiple tasks.
C. A job contains multiple stages, and each stage contains multiple tasks.
D. A job contains multiple tasks, and each task contains multiple stages.

Answer: C

Explanation:
A Spark job is triggered by an action (e.g., count, show).
The job is broken into stages, typically one per shuffle boundary.
Each stage is divided into multiple tasks, which are distributed across worker nodes.

NEW QUESTION # 120
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() - default micro-batch trigger without interval.
B. Uses trigger(continuous='5 seconds') - continuous processing mode.
C. Uses trigger(processingTime='5 seconds') - correct micro-batch trigger with interval.
D. Uses trigger(processingTime=5000) - invalid, as processingTime expects a string.

Answer: C

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 # 121
......

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