Question 1

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 2

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

Question 3

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 4

A Spark application needs to read multiple Parquet files from a directory where the files have differing but compatible schemas. The data engineer wants to create a DataFrame that includes all columns from all files. Which code should the data engineer use to read the Parquet files and include all columns using Apache Spark?

Options :

A : spark.read.parquet("/data/parquet/")

B : spark.read.option("mergeSchema", True).parquet("/data/parquet/")

C : spark.read.format("parquet").option("inferSchema", "true").load("/data/parquet/")

D : spark.read.parquet("/data/parquet/").option("mergeAllCols", True)

Answer: B

Question 5

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 :

Any data arriving more than 10 minutes after the watermark threshold will be ignored and not included in the aggregation.

C :

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.

D :

The watermark ensures that late data arriving within 10 minutes of the latest event_time will be processed and included in the windowed aggregation.

Answer: B

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