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Question 1

A Spark developer is developing a Spark application to monitor task performance across a cluster. One requirement is to track the maximum processing time for tasks on each worker node and consolidate this information on the driver for further analysis. Which technique should the developer use?

Options :

A : Broadcast a variable to share the maximum time among workers.

B : Configure the Spark UI to automatically collect maximum times.

C : Use an RDD action like reduce() to compute the maximum time.

D : Use an accumulator to record the maximum time on the driver.

Answer: C

Question 2

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?

Options :

A : By configuring the option recoveryLocation during SparkSession initialization.

B : By configuring the option checkpointLocation during readStream.

C : By configuring the option checkpointLocation during writeStream.

D : By configuring the option recoveryLocation during writeStream.

Answer: C

Question 3

Which feature of Spark Connect should be considered when designing an application that plans to enable remote interaction with a Spark cluster?

Options :

A : It is primarily used for data ingestion into Spark from external sources.

B : It provides a way to run Spark applications remotely in any programming language.

C : It can be used to interact with any remote cluster using the REST API.

D : It allows for remote execution of Spark jobs.

Answer: D

Question 4

Given a DataFramedfthat has 10 partitions, after running the code: result = df.coalesce(20) How many partitions will the result DataFrame have?

Options :

A : 10

B : Same number as the cluster executors

C : 1

D : 20

Answer: A

Question 5

A data scientist is working on a large dataset in Apache Spark using PySpark. The data scientist has a DataFramedfwith columnsuser_id,product_id, andpurchase_amountand needs to perform some operations on this data efficiently. Which sequence of operations results in transformations that require a shuffle followed by transformations that do not?

Options :

A : df.filter(df.purchase_amount > 100).groupBy("user_id").sum("purchase_amount")

B : df.withColumn("discount", df.purchase_amount * 0.1).select("discount")

C : df.withColumn("purchase_date", current_date()).where("total_purchase > 50")

D : df.groupBy("user_id").agg(sum("purchase_amount").alias("total_purchase")).repartition(10)

Answer: D