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Question 1

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 2

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 3

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 4

A Spark application suffers from too many small tasks due to excessive partitioning. How can this be fixed without a full shuffle?

Options :

A : Use the distinct() transformation to combine similar partitions

B : Use the coalesce() transformation with a lower number of partitions

C : Use the sortBy() transformation to reorganize the data

D : Use the repartition() transformation with a lower number of partitions

Answer: B

Question 5

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