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Comprehensive and Detailed Explanation From Exact Extract:

Shuffling occurs in operations likegroupBy,reduceByKey, orjoin—which cause data to be moved across partitions. Therepartition()operation can also cause a shuffle, but in this context, it follows an aggregation.

InOption D, thegroupByfollowed byaggresults in a shuffle due to grouping across nodes.

Therepartition(10)is a partitioning transformation but does not involve a new shuffle since the data is already grouped.

This sequence — shuffle (groupBy) followed by non-shuffling (repartition) — is correct.

Option A does the opposite: thefilterdoes not cause a shuffle, butgroupBydoes — this makes it the wrong order.

Comprehensive and Detailed Explanation From Exact Extract:

When you convert a largepyspark.pandas(aka Pandas API on Spark) DataFrame to a local Pandas DataFrame using.toPandas(), Spark collects all partitions to the driver.

From the Spark documentation:

“Be careful when converting large datasets to Pandas. The entire dataset will be pulled into the driver’s memory.”

Thus, for large datasets, this can cause memory overflow or out-of-memory errors on the driver.

Final Answer: D

Comprehensive and Detailed Explanation From Exact Extract:

The correct approach to perform a parallelizedgroupByoperation across Spark worker nodes using Pandas API is viaapplyInPandas. This function enables grouped map operations using Pandas logic in a distributed Spark environment. It applies a user-defined function to each group of data represented as a Pandas DataFrame.

As per the Databricks documentation:

"applyInPandas()allows for vectorized operations on grouped data in Spark. It applies a user-defined function to each group of a DataFrame and outputs a new DataFrame. This is the recommended approach for using Pandas logic across grouped data with parallel execution."

Option A is correct and achieves this parallel execution.

Option B (mapInPandas) applies to the entire DataFrame, not grouped operations.

Option C uses built-in aggregation functions, which are efficient but not customizable with Pandas logic.

Option D creates a scalar Pandas UDF which does not perform a group-wise transformation.

Therefore, to run agroupBywith parallel Pandas logic on Spark workers, Option A usingapplyInPandasis the only correct answer.

Comprehensive and Detailed Explanation From Exact Extract:

To filter rows based on a condition and display them in Spark, usefilter(...).show():

employees_df.filter(employees_df.tenure >= 5).show()

Option A is correct and shows the results.

Option B filters but doesn’t display them.

Option C uses Python’s built-infilter, not Spark.

Option D collects the results to the driver, which is unnecessary if.show()is sufficient.

Final Answer: A

Comprehensive and Detailed Explanation From Exact Extract:

The functiondate_sub(start, days)subtracts the number of days from the start date. If a negative number is passed, the behavior becomes a date addition.

Example:

SELECT date_sub('2024-05-01', -5)

-- Returns: 2024-05-06

So, a negative value effectively adds the absolute number of days to the date.

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.

Comprehensive and Detailed Explanation From Exact Extract:

The Spark UI is the standard and most effective way to inspect executor logs, task time, input size, and shuffles.

From the Databricks documentation:

“You can monitor job execution via the Spark Web UI. It includes detailed logs and metrics, including task-level execution time, shuffle reads/writes, and executor memory usage.”

(Source: Databricks Spark Monitoring Guide)

Option A is incorrect: logs are not guaranteed to be in/tmp, especially in cloud environments.

B.—verbosehelps during job submission but doesn't give detailed executor logs.

D.spark-sqlis a CLI tool for running queries, not for inspecting logs.

Hence, the correct method is using the Spark UI → Stages tab → Executor logs.

Comprehensive and Detailed Explanation From Exact Extract:

In Apache Spark, broadcast variables are used to efficiently distribute large, read-only data to all worker nodes. However, broadcasting very large datasets can lead to memory issues on executors if the data does not fit into the available memory.

According to the Spark documentation:

"Broadcast variables allow the programmer to keep a read-only variable cached on each machine rather than shipping a copy of it with tasks. This can greatly reduce the amount of data sent over the network."

However, it also notes:

"Using the broadcast functionality available in SparkContext can greatly reduce the size of each serialized task, and the cost of launching a job over a cluster. If your tasks use any large object from the driver program inside of them (e.g., a static lookup table), consider turning it into a broadcast variable."

But caution is advised when broadcasting large datasets:

"Broadcasting large variables can cause out-of-memory errors if the data does not fit in the memory of each executor."

Therefore, if the broadcasted DataFrame containing millions of rows exceeds the memory capacity of the executors, the job may fail due to memory constraints.

Comprehensive and Detailed Explanation From Exact Extract:

In Spark, the default join type is an inner join, which returns only the rows with matching keys in both DataFrames. To retain all records from the left DataFrame (purch_df) and include matching records from the right DataFrame (cust_df), a left outer join should be used.

By specifying the join type as'left', the modified code ensures that all records frompurch_dfare preserved, and matching records fromcust_dfare included. Records inpurch_dfwithout a corresponding match incust_dfwill havenullvalues for the columns fromcust_df.

This approach is consistent with standard SQL join operations and is supported in PySpark's DataFrame API.

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.