Spark Overview for Scala Analytics Quiz Answers

Get Spark Overview for Scala Analytics Quiz Answers

The “Spark Overview for Scala Analytics” course will cover the history of Spark and how it came to be, how to build applications with Spark, establish an understanding of RDDs and DataFrames, and other advanced Spark topics. Apache Spark™ is a fast and general engine for large-scale data processing, with built-in modules for streaming, SQL, machine learning and graph processing. Having finished this class, a student would be prepared to leverage the core RDD and DataFrame APIs to perform analytics on datasets.

This course is meant to be an overview of Spark and its associated ecosystem.
There are 5 modules to this course.
1. What is Spark
2. Introduction to RDDs
3. Introduction to DataFrames
4. Advanced Spark Topics
5. Introduction to Spark MLlib

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Final Exam

Question: Which language is not supported by Spark?

  • SQL
  • C
  • Java
  • Python
  • Scala

Question: What does RDD stand for?

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

Question: The Spark Web Console is used to:

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

Question: The RDD flatMap method does what?

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

Question: Shuffling is used to:

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

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

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

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

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

Question: The sequence of transformation and action method calls:

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

Question: The Inverted Index computes what?

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

Question: Broadcast variables are used for what?

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

Question: Accumulators are used for what?

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

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

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

Question: DataFrames support the following operations:

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

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

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

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

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

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

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

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

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

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

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

Question: In Spark Streaming, a DStream is:

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

Question: The batch interval:

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

Conclusion:

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