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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 needs to write a Streaming DataFrame as Parquet files. Given the code:

Given the code:

Options :

A :

format("parquet") .option("location", "path/to/destination/dir")

B :

CopyEdit .option("format", "parquet") .option("destination", "path/to/destination/dir")

C :

.option("format", "parquet") .option("location", "path/to/destination/dir")

D :

format("parquet") .option("path", "path/to/destination/dir")

Answer: D

Question 3

A Spark engineer must select an appropriate deployment mode for the Spark jobs. What is the benefit of using cluster mode in Apache Spark™?

Options :

A :

In cluster mode, resources are allocated from a resource manager on the cluster, enabling better performance and scalability for large jobs

B :

In cluster mode, the driver is responsible for executing all tasks locally without distributing them across the worker nodes.

C :

In cluster mode, the driver runs on the client machine, which can limit the application's ability to handle large datasets efficiently.

D :

In cluster mode, the driver program runs on one of the worker nodes, allowing the application to fully utilize the distributed resources of the cluster.

Answer: D

Question 4

A data engineer is working ona Streaming DataFrame streaming_df with the given streaming data:

Which operation is supported with streamingdf ?

Options :

A : streaming_df. select (countDistinct ("Name") )

B : streaming_df.groupby("Id") .count ()

C : streaming_df.orderBy("timestamp").limit(4)

D : streaming_df.filter (col("count") < 30).show()

Answer: D

Question 5

Which Spark configuration controls the number of tasks that can run in parallel on the executor? Options:

Options :

A : spark.executor.cores

B : spark.task.maxFailures

C : spark.driver.cores

D : spark.executor.memory

Answer: A