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Given the code:
df = spark.read.csv("large_dataset.csv")
filtered_df = df.filter(col("error_column").contains("error"))
mapped_df = filtered_df.select(split(col("timestamp")," ").getItem(0).alias("date"), lit(1).alias("count")) reduced_df = mapped_df.groupBy("date").sum("count") reduced_df.count() reduced_df.show() At which point will Spark actually begin processing the data?
A. When the groupBy transformation is applied
B. When the show action is applied
C. When the count action is applied
D. When the filter transformation is applied
Answer: C
Explanation:
Spark uses lazy evaluation. Transformations like filter, select, and groupBy only define the DAG (Directed Acyclic Graph). No execution occurs until an action is triggered.
The first action in the code is:reduced_df.count()
So Spark starts processing data at this line.
Reference:Apache Spark Programming Guide - Lazy Evaluation
NEW QUESTION # 59
6 of 55.
Which components of Apache Spark's Architecture are responsible for carrying out tasks when assigned to them?
A. Driver Nodes
B. CPU Cores
C. Executors
D. Worker Nodes
Answer: C
Explanation:
In Spark's distributed architecture:
The Driver Node coordinates the execution of a Spark application. It converts the logical plan into a physical plan of stages and tasks.
The Executors, running on Worker Nodes, are responsible for executing tasks assigned by the driver and storing data (in memory or disk) during execution.
Key point:
Executors are the active agents that perform the actual computations on data partitions. Each executor runs multiple tasks in parallel using available CPU cores.
Why the other options are incorrect:
A (Driver Nodes): The driver schedules tasks; it doesn't execute them.
C (CPU Cores): CPU cores execute within executors, but they are hardware, not Spark architectural components.
D (Worker Nodes): Worker nodes host executors but do not directly execute tasks; executors do.
Reference (Databricks Apache Spark 3.5 - Python / Study Guide):
Spark Architecture Components - Driver, Executors, Cluster Manager, Worker Nodes.
Databricks Exam Guide (June 2025): Section "Apache Spark Architecture and Components" - describes the roles of driver and executor nodes in distributed processing.
NEW QUESTION # 60
A data engineer wants to process a streaming DataFrame that receives sensor readings every second with columnssensor_id,temperature, andtimestamp. The engineer needs to calculate the average temperature for each sensor over the last 5 minutes while the data is streaming.
Which code implementation achieves the requirement?
Options from the images provided:
A.
B.
C.
D.
Answer: B
Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
The correct answer isDbecause it uses proper time-based window aggregation along with watermarking, which is the required pattern in Spark Structured Streaming for time-based aggregations over event-time data.
From the Spark 3.5 documentation on structured streaming:
"You can define sliding windows on event-time columns, and usegroupByalong withwindow()to compute aggregates over those windows. To deal with late data, you usewithWatermark()to specify how late data is allowed to arrive." (Source:Structured Streaming Programming Guide) In optionD, the use of:
python
CopyEdit
groupBy("sensor_id", window("timestamp","5 minutes"))
agg(avg("temperature").alias("avg_temp"))
ensures that for eachsensor_id, the average temperature is calculated over 5-minute event-time windows. To complete the logic, it is assumed thatwithWatermark("timestamp", "5 minutes")is used earlier in the pipeline to handle late events.
Explanation of why other options are incorrect:
Option AusesWindow.partitionBywhich applies to static DataFrames or batch queries and is not suitable for streaming aggregations.
Option Bdoes not apply a time window, thus does not compute the rolling average over 5 minutes.
Option Cincorrectly applieswithWatermark()after an aggregation and does not include any time window, thus missing the time-based grouping required.
Therefore,Option Dis the only one that meets all requirements for computing a time-windowed streaming aggregation.
NEW QUESTION # 61
14 of 55.
A developer created a DataFrame with columns color, fruit, and taste, and wrote the data to a Parquet directory using:
df.write.partitionBy("color", "taste").parquet("/path/to/output")
What is the result of this code?
A. It appends new partitions to an existing Parquet file.
B. It stores all data in a single Parquet file.
C. It throws an error if there are null values in either partition column.
D. It creates separate directories for each unique combination of color and taste.
Answer: D
Explanation:
When writing a DataFrame using .partitionBy() in Spark, the data is physically organized into directory structures corresponding to unique combinations of the partition columns.
Example:
/path/to/output/color=Red/taste=Sweet/part-0001.parquet
/path/to/output/color=Green/taste=Sour/part-0002.parquet
This structure improves query performance by pruning partitions when filtering on these columns.
Why the other options are incorrect:
A: Appending requires .mode("append"), which isn't used here.
B: Null values in partition columns are handled; they don't raise errors.
D: Partitioning prevents storing all data in a single file.
Reference:
PySpark DataFrameWriter API - partitionBy() and .parquet() methods.
Databricks Exam Guide (June 2025): Section "Using Spark SQL" - partitioning and writing optimized output files.
NEW QUESTION # 62
A Spark developer is building an app to monitor task performance. They need to track the maximum task processing time per worker node and consolidate it on the driver for analysis.
Which technique should be used?
A. Broadcast a variable to share the maximum time among workers
B. Use an accumulator to record the maximum time on the driver
C. Use an RDD action like reduce() to compute the maximum time
D. Configure the Spark UI to automatically collect maximum times
Answer: C
Explanation:
The correct way to aggregate information (e.g., max value) from distributed workers back to the driver is using RDD actions such as reduce() or aggregate().
From the documentation:
"To perform global aggregations on distributed data, actions like reduce() are commonly used to collect summaries such as min/max/avg." Accumulators (Option B) do not support max operations directly and are not intended for such analytics.
Broadcast (Option C) is used to send data to workers, not collect from them.
Spark UI (Option D) is a monitoring tool - not an analytics collection interface.
Final answer: A
NEW QUESTION # 63
......
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