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

A developer initializes a SparkSession:

spark = SparkSession.builder \ .appName("Analytics Application") \ .getOrCreate() Which statement describes thesparkSparkSession?ThegetOrCreate()method explicitly destroys any existing SparkSession and creates a new one.

Options :

A : ThegetOrCreate()method explicitly destroys any existing SparkSession and creates a new one.

B :

A SparkSession is unique for eachappName, and callinggetOrCreate()with the same name will return an existing SparkSession once it has been created.

C : If a SparkSession already exists, this code will return the existing session instead of creating a new one.

D : A new SparkSession is created every time thegetOrCreate()method is invoked.

Answer: C

Question 3

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

Question 4

A data scientist of an e-commerce company is working with user data obtained from its subscriber database and has stored the data in a DataFrame df_user. Before further processing the data, the data scientist wants to create another DataFrame df_user_non_pii and store only the non-PII columns in this DataFrame. The PII columns in df_user are first_name, last_name, email, and birthdate. Which code snippet can be used to meet this requirement?

Options :

A : df_user_non_pii = df_user.drop("first_name", "last_name", "email", "birthdate")

B : df_user_non_pii = df_user.drop("first_name", "last_name", "email", "birthdate")

C : df_user_non_pii = df_user.dropfields("first_name", "last_name", "email", "birthdate")

D : df_user_non_pii = df_user.dropfields("first_name, last_name, email, birthdate")

Answer: A

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

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