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Databricks Certified Associate Developer for Apache Spark 3.5 - Python Sample Questions (Q45-Q50):

NEW QUESTION # 45
A Data Analyst is working on the DataFramesensor_df, which contains two columns:
Which code fragment returns a DataFrame that splits therecordcolumn into separate columns and has one array item per row?
A)

B)

C)

D)

A. exploded_df = exploded_df.select(
"record_datetime",
"record_exploded.sensor_id",
"record_exploded.status",
"record_exploded.health"
)
exploded_df = sensor_df.withColumn("record_exploded", explode("record"))
B. exploded_df = exploded_df.select("record_datetime", "record_exploded")
C. exploded_df = sensor_df.withColumn("record_exploded", explode("record")) exploded_df = exploded_df.select("record_datetime", "sensor_id", "status", "health")
D. exploded_df = exploded_df.select(
"record_datetime",
"record_exploded.sensor_id",
"record_exploded.status",
"record_exploded.health"
)
exploded_df = sensor_df.withColumn("record_exploded", explode("record"))

Answer: D

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
To flatten an array of structs into individual rows and access fields within each struct, you must:
Useexplode()to expand the array so each struct becomes its own row.
Access the struct fields via dot notation (e.g.,record_exploded.sensor_id).
Option C does exactly that:
First, explode therecordarray column into a new columnrecord_exploded.
Then, access fields of the struct using the dot syntax inselect.
This is standard practice in PySpark for nested data transformation.
Final Answer: C

NEW QUESTION # 46
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(processingTime='5 seconds') - correct micro-batch trigger with interval.
C. Uses trigger(continuous='5 seconds') - continuous processing mode.
D. Uses trigger(processingTime=5000) - invalid, as processingTime expects a string.

Answer: B

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 # 47
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 will store as much data as possible in memory and spill the rest to disk when memory is full, continuing processing with performance overhead.
B. 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.
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 splits the DataFrame evenly between memory and disk, ensuring balanced storage utilization.

Answer: A

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 # 48
Given the following code snippet inmy_spark_app.py:

What is the role of the driver node?

A. The driver node orchestrates the execution by transforming actions into tasks and distributing them to worker nodes
B. The driver node only provides the user interface for monitoring the application
C. The driver node stores the final result after computations are completed by worker nodes
D. The driver node holds the DataFrame data and performs all computations locally

Answer: A

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
In the Spark architecture, the driver node is responsible for orchestrating the execution of a Spark application.
It converts user-defined transformations and actions into a logical plan, optimizes it into a physical plan, and then splits the plan into tasks that are distributed to the executor nodes.
As per Databricks and Spark documentation:
"The driver node is responsible for maintaining information about the Spark application, responding to a user's program or input, and analyzing, distributing, and scheduling work across the executors." This means:
Option A is correct because the driver schedules and coordinates the job execution.
Option B is incorrect because the driver does more than just UI monitoring.
Option C is incorrect since data and computations are distributed across executor nodes.
Option D is incorrect; results are returned to the driver but not stored long-term by it.
Reference: Databricks Certified Developer Spark 3.5 Documentation # Spark Architecture # Driver vs Executors.

NEW QUESTION # 49
Given this code:

.withWatermark("event_time","10 minutes")
.groupBy(window("event_time","15 minutes"))
.count()
What happens to data that arrives after the watermark threshold?
Options:

A. Records that arrive later than the watermark threshold (10 minutes) will automatically be included in the aggregation if they fall within the 15-minute window.
B. Data arriving more than 10 minutes after the latest watermark will still be included in the aggregation but will be placed into the next window.
C. The watermark ensures that late data arriving within 10 minutes of the latest event_time will be processed and included in the windowed aggregation.
D. Any data arriving more than 10 minutes after the watermark threshold will be ignored and not included in the aggregation.

Answer: D

Explanation:
According to Spark's watermarking rules:
"Records that are older than the watermark (event time < current watermark) are considered too late and are dropped." So, if a record'sevent_timeis earlier than (max event_time seen so far - 10 minutes), it is discarded.
Reference:Structured Streaming - Handling Late Data

NEW QUESTION # 50
......

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