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Spark Fundamentals I Cognitive Class Exam Quiz Answers

Spark Fundamentals I Cognitive Class Certification Answers

Question 1: What gives Spark its speed advantage for complex applications?

  • Spark can cover a wide range of workloads under one system
  • Various libraries provide Spark with additional functionality
  • Spark extends the MapReduce model
  • Spark makes extensive use of in-memory computations
  • All of the above

Question 2: For what purpose would an Engineer use Spark? Select all that apply.

  • Analyzing data to obtain insights
  • Programming with Spark’s API
  • Transforming data into a useable form for analysis
  • Developing a data processing system
  • Tuning an application for a business use case

Question 3: Which of the following statements are true of the Resilient Distributed Dataset (RDD)? Select all that apply.

  • There are three types of RDD operations.
  • RDDs allow Spark to reconstruct transformations
  • RDDs only add a small amount of code due to tight integration
  • RDD action operations do not return a value
  • RDD is a distributed collection of elements parallelized across the cluster.

Question 1: Which of the following methods can be used to create a Resilient Distributed Dataset (RDD)? Select all that apply.

  • Creating a directed acyclic graph (DAG)
  • Parallelizing an existing Spark collection
  • Referencing a Hadoop-supported dataset
  • Using data that resides in Spark
  • Transforming an existing RDD to form a new one

Question 2: What happens when an action is executed?

  • The driver sends code to be executed on each block
  • Executors prepare the data for operation in parallel
  • A cache is created for storing partial results in memory
  • Data is partitioned into different blocks across the cluster
  • All of the above

Question 3: Which of the following statements is true of RDD persistence? Select all that apply.

  • Persistence through caching provides fault tolerance
  • Future actions can be performed significantly faster
  • Each partition is replicated on two cluster nodes
  • RDD persistence always improves space efficiency
  • By default, objects that are too big for memory are stored on the disk

Question 1: What is Spark Context?

  • A tool for linking to nodes
  • A tool that provides fault tolerance
  • A programming language for applications
  • The built-in shell for the Spark engine
  • An object that represents the connection to a Spark cluster

Question 2: Which of the following methods can be used to pass functions to Spark? Select all that apply.

  • Transformations and actions
  • Passing by reference
  • Static methods in a global singleton
  • Import statements
  • Anonymous function syntax

Question 3: Which of the following is a main component of a Spark application’s source code?

  • Import statements
  • Business Logic
  • Spark Context object
  • Transformations and actions
  • All of the above

Question 1: Which of the following is NOT an example of a Spark library?

  • MLlib
  • Hive
  • Spark SQL
  • GraphX
  • Spark Streaming

Question 2: From which of the following sources can Spark Streaming receive data? Select all that apply.

  • Kafka
  • JSON
  • Parquet
  • HDFS
  • Hive

Question 3: In Spark Streaming, processing begins immediately when an element of the application is executed. True or false?

  • True
  • False

Question 1: hich of the following is a main component of a Spark cluster? Select all that apply.

  • Driver Program
  • Spark Context
  • Cluster Manager
  • Worker Node
  • Cache

Question 2: What are the main locations for Spark configuration? Select all that apply.

  • The Spark Conf object
  • The Spark Shell
  • Executor Processes
  • Environment variables
  • Logging properties

Question 3: Which of the following techniques can improve Spark performance? Select all that apply.

  • Scheduler Configuration
  • Memory Tuning
  • Data Serialization
  • Using Broadcast variables
  • Using nested structures

Question 1: Which of the following is a type of Spark RDD operation? Select all that apply.

  • Parallelization
  • Action
  • Persistence
  • Transformation
  • Evaluation

Question 2: Spark must be installed and run on top of a Hadoop cluster. True or false

  • True
  • False

Question 3: following operations will work improperly when using a Combiner?

  • Average
  • Maximum
  • Minimum
  • Count
  • All of the above operations will work properly

Question 4: Spark supports which of the following libraries?

  • Spark SQL
  • MLlib
  • GraphX
  • Spark Streaming
  • All of the above

Question 5: Spark supports which of the following programming languages?

  • Scala, Perl, Java
  • Scala, Java, C++, Python, Perl
  • Scala, Python, Java, R
  • Java and Scala
  • C++ and Python

Question 6: A transformation is evaluated immediately. True or false?

  • True
  • False

Question 7: Which storage level does the cache() function use?

  • MEMORY_ONLY
  • MEMORY_ONLY_SER
  • MEMORY_AND_DISK
  • MEMORY_AND_DISK_SER

Question 8: Which of the following statements does NOT describe accumulators?

  • They can only be added through an associative operation
  • Programmers can extend them beyond numeric types
  • They can only be read by the driver
  • They are read-only
  • They implement counters and sums

Question 9: You must explicitly initialize the Spark Context when creating a Spark application. True or false?

  • True
  • False

Question 10: The “local” parameter can be used to specify the number of cores to use for the application. True or false?

  • True
  • False

Question 11: Spark applications can ONLY be packaged using one, specific build tool. True or false?

  • True
  • False

Question 12: Which of the following parameters of the “spark-submit” script determine where the application will run?

  • –master
  • –conf
  • –class
  • –deploy-mode
  • None of the above

Question 13: Which of the following is NOT supported as a cluster manager?

  • Mesos
  • Spark
  • YARN
  • Helix
  • All of the above are supported

Question 14: Spark SQL allows relational queries to be expressed in which of the following?

  • Scala, SQL, and HiveQL
  • Scala and HiveQL
  • Scala and SQL
  • SQL only
  • HiveQL only

Question 15:  Spark Streaming processes live streaming data in real-time. True or false?

  • True
  • False

Question 16: The MLlib library contains which of the following algorithms?

  • Classification
  • Regression
  • Clustering
  • Dimensionality Reduction
  • All of the above

Question 17: What is the purpose of the GraphX library?

  • To create a visual representation of the data
  • To generate data-parallel models
  • To create a visual representation of a directed acyclic graph (DAG)
  • To perform graph-parallel computations
  • To convert from data-parallel to graph-parallel algorithms

Question 18: Which list describes the correct order of precedence for Spark configuration, from highest to lowest?

  • Flags passed to spark-submit, values in spark-defaults.conf, properties set on SparkConf
  • Properties set on SparkConf, values in spark-defaults.conf, flags passed to spark-submit
  • Values in spark-defaults.conf, properties set on SparkConf, flags passed to spark-submit
  • Properties set on SparkConf, flags passed to spark-submit, values in spark-defaults.conf
  • Values in spark-defaults.conf, flags passed to spark-submit, properties set on SparkConf

Question 19: Spark monitoring can be performed with external tools. True or false?

  • True
  • False

Question 20: Which serialization libraries are supported in Spark? Select all that apply.

  • Apache Avro
  • Java Serialization
  • Protocol Buffers
  • Kyro Serialization
  • TPL

Introduction to Spark Fundamentals I

“Spark Fundamentals I” serves as a foundational stepping stone into understanding Apache Spark, a powerful open-source distributed computing system primarily used for big data processing and analytics. In this introductory course, you’ll explore the core concepts and components of Spark, gaining insight into its architecture, capabilities, and practical applications.

Here’s an overview of what you might expect to learn in Spark Fundamentals I:

  1. Introduction to Spark: Understand what Spark is, its evolution, and why it’s widely used in the industry for big data processing tasks.
  2. Spark Architecture: Delve into Spark’s architecture, including its primary components such as Spark Core, Spark SQL, Spark Streaming, MLlib (Machine Learning Library), and GraphX (Graph Processing).
  3. Resilient Distributed Dataset (RDD): Learn about RDDs, the fundamental data structure in Spark that represents distributed collections of objects. Understand RDD transformations and actions, and how they enable parallel computation.
  4. Spark Applications: Explore how to develop and deploy Spark applications, including setting up the development environment, writing Spark code in various programming languages such as Scala, Python, or Java, and running Spark applications on a cluster.
  5. Spark SQL: Gain insights into Spark SQL, which provides a SQL-like interface for querying structured data both inside Spark programs and from external data sources. Learn how to perform SQL queries directly on Spark data structures.
  6. Spark Streaming: Get introduced to Spark Streaming, which enables real-time stream processing in Spark. Understand its architecture, how to create streaming applications, and common use cases.
  7. MLlib: Discover MLlib, Spark’s machine learning library, which provides scalable implementations of various machine learning algorithms. Learn how to train models, perform predictions, and evaluate model performance using MLlib.
  8. GraphX: Explore GraphX, Spark’s graph processing library, which enables graph computation and analysis directly within the Spark framework. Understand graph representation, basic operations, and common graph algorithms.

Throughout the course, you’ll likely engage in hands-on exercises, coding assignments, and real-world examples to solidify your understanding of Spark Fundamentals I. By the end, you should have a solid foundation in Apache Spark, ready to tackle more advanced topics and applications in big data processing and analytics.

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