Spark Overview for Scala Analytics cognitive class Exam Answers:-

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Course Name:- Spark Overview for Scala Analytics

Spark Overview for Scala Analytics cognitive class Final Exam Answers:-

Question 1. Which language is not supported by Spark?

• SQL
• Scala
• Java
• C
• Python

Question 2. What does RDD stand for?

• REPL Definition and Description
• Resilient Distributed Dataset
• Reader Distribution Defined
• Resilient Documented DataFrame
• Read, Distribute, Delete

Question 3. The Spark Web Console is used to:

• Edit Spark code
• Integrate Spark with third-party tools
• Examine data produced by Spark jobs
• Monitor running Spark jobs
• Submit Spark jobs

Question 4. The RDD flatMap method does what?

• Transform each input record to zero or more output records
• Transforms each input record to a new output record
• Combines all records into a value
• Reads a data source
• None of the above

Question 5. Shuffling is used to:

• Move data between stages
• Design where partitions are written to disk
• Move tasks to the appropriate nodes in a cluster
• Sort data when that’s requested
• All of the above

Question 6. Transformation methods have one or more of the following characteristics:

• One and only one record is output for each input record
• Lazy (delayed) evaluation
• Their results are cached in memory
• Eager (immediate) evaluation
• None of the above

Question 7. Action methods have one or more of the following characteristics:

• Eager (immediate) evaluation
• Return a new RDD
• Do not support type inference
• Must be the first methods in a sequence of methods
• All of the above

Question 8. The sequence of transformation and action method calls:

• Is run in parallel for each data partition
• Starts with some data and returns or outputs new data
• Is decomposed into stages
• Forms a directed, acyclic graph
• All of the above

Question 9. The Inverted Index computes what?

• The records sorted descending by a key
• The minimum, maximum, and average counts for words in the corpus
• Output records with words as keys and document ids and counts as values
• A table of contents for a corpus of documents
• All of the above

Question 10. Broadcast variables are used for what?

• To send all RDD data to the tasks
• Print messages to the Spark web console
• Send metrics to a monitoring tool
• Share read-only data with all tasks in an efficient way
• None of the above

Question 11. Accumulators are used for what?

• Collect the results of the Spark job
• Aggregate extra data across all tasks
• Manage streams in Spark Streaming
• Send metrics to a monitoring tool
• All of the above

Question 12. DataFrames have one or more of the following characteristics:

• Support for SQL queries
• Handle data when its structure is known and consistent
• Excellent runtime performance
• Support HIVE integration
• All of the above

Question 13. DataFrames support the following operations:

• Non-equi joins
• Reduce
• Delete
• Group by
• All of the above

Question 14. If I have a dataframe “person” with a field “age”, which of the following expressions can never be used to reference that field?

• “age”
• $”age”
• person($”age”)
• person(“age”)
• All of the above are valid

Question 15. If I want to write a SQL query over a DataFrame, I have to call the following method first:

• Persist
• Write
• RegisterTempTable
• Map
• None of the above

Question 16. Which one of the following kinds of joins is not supported?

• Left outer join
• Inner join
• Right outer join
• Left semijoin
• All are supported

Question 17. The DataFrame expression “persons.select($”age”).where($”age” > 21)” returns:

• A DataFrame
• A Scala Vector[Int]
• A ResultSet
• A RDD
• None of the above

Question 18. In Hive, an external table has the property:

• It’s data is not managed by Hive
• It’s format is defined elsewhere
• It’s schema is defined elsewhere
• It is visible to all users of Hive
• All of the above

Question 19. In Spark Streaming, a DStream is:

• A fixed-sized batch of incoming data
• A connector to a socket
• A sequence of RDDs
• A collection of DataFrames
• None of the above

Question 20. The batch interval:

• starts at a user-specified value and adjusts in response to load
• is the number of events to capture per batch
• is the size of each data “chunk” returned by a DataFrame query
• is determined dynamically by Spark
• is the number of seconds to capture data per batch