A Spark developer is debugging a job that processes a stream of sensor readings. The job uses a map transformation to convert raw readings to a structured format. The developer notices that the transformation is not being executed when they call map. Why is this happening?

Because the data source is not properly connected

Because the map function is not correctly defined

Because the driver is not sending the task to the executors

Because map is a transformation and is lazily evaluated until an action is called

What is the primary difference between transformations and actions in Spark?

Transformations return a value to the driver; actions return a new RDD.

Transformations are lazy and return a new RDD/DataFrame; actions trigger computation and return a value.

Transformations are eager and actions are lazy.

Transformations can only be applied to RDDs, while actions can only be applied to DataFrames.

A team has a DataFrame containing sales records with columns 'product_id', 'amount', and 'sale_date'. They need to compute the total sales amount per product for the last 30 days. They want to use Spark SQL for this task. Which approach should they take?

Convert the DataFrame to an RDD and use a map-reduce operation

Register the DataFrame as a temporary view and run a SQL query with a WHERE clause and GROUP BY

Write the DataFrame to a Hive table and query it with HiveQL

Use the DataFrame API with a filter and a groupBy, but do not register a view

Which statement correctly describes the role of executors in a Spark cluster?

Executors allocate cluster resources and manage the application's lifecycle.

Executors run tasks and store data for the application in memory or disk.

Executors coordinate the overall job and schedule tasks on workers.

Executors only store data and do not perform any computation.

A data scientist needs to perform a complex, custom transformation on a dataset that involves iterating over each record and maintaining a mutable accumulator that is not a standard aggregation. The dataset is small enough to fit in memory on a single node. Which Spark API should they choose?

Spark SQL with a window function

Dell Data Science Optimize

Domain 2Objective 5

Spark DATA-SCIENCE-OPTIMIZE Practice Questions (Page 3)

Part of the Hadoop Ecosystem and NoSQL domain, which accounts for 15% of the DATA-SCIENCE-OPTIMIZE exam.

25questions here

5free pages

6concepts

15%of the exam

Questions 11–15

11application · medium
A Spark developer is debugging a job that processes a stream of sensor readings. The job uses a map transformation to convert raw readings to a structured format. The developer notices that the transformation is not being executed when they call map. Why is this happening?
Select an answer first
12foundation · easy
What is the primary difference between transformations and actions in Spark?
Select an answer first
13application · medium
A team has a DataFrame containing sales records with columns 'product_id', 'amount', and 'sale_date'. They need to compute the total sales amount per product for the last 30 days. They want to use Spark SQL for this task. Which approach should they take?
Select an answer first
14foundation · easy
Which statement correctly describes the role of executors in a Spark cluster?
Select an answer first
15application · medium
A data scientist needs to perform a complex, custom transformation on a dataset that involves iterating over each record and maintaining a mutable accumulator that is not a standard aggregation. The dataset is small enough to fit in memory on a single node. Which Spark API should they choose?
Select an answer first

Finished these 5 questions?

Review the revealed explanations, or continue through the curriculum.

Next 5 questions →Back to DATA-SCIENCE-OPTIMIZE

Free Basic Practice is a study aid with revealable answers — not a scored exam. Examers.io is independent and not affiliated with or endorsed by Dell Technologies. “DATA-SCIENCE-OPTIMIZE” is a trademark of its owner, used for identification only.