Spark Fundamentals I Cognitive Class Exam Quiz Answers

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Enroll Here: Spark Fundamentals I Cognitive Class Exam Quiz Answers

Spark Fundamentals I Cognitive Class Certification Answers

Module 1: Introduction to Spark

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

• Spark extends the MapReduce model
• Various libraries provide Spark with additional functionality
• Spark can cover a wide range of workloads under one system
• 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.

Module 2: Resilient Distributed Dataset and DataFrames

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?

• Executors prepare the data for operation in parallel
• The driver sends code to be executed on each block
• 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

Module 3: Spark Application Programming

Question 1: What is SparkContext?

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

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?

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

Module 4: Introduction to the Spark Libraries

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

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

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

Module 5: Spark Configuration, Monitoring and Tuning

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

• Driver Program
• SparkContext
• Cluster Manager
• Worker node
• Cache

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

• The SparkConf 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

Spark Fundamentals I Final Exam Answers – Cognitive Class

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: Which of the following operations will work improperly when using a Combiner?

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

Question 4: Spark supports which of the following libraries?

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

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

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

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

• True
• False

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

• MEMORY_AND_DISK_SER
• MEMORY_AND_DISK
• MEMORY_ONLY_SER
• MEMORY_ONLY

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

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

Question 9: You must explicitly initialize the SparkContext 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?

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

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

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

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

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

• Dimensionality Reduction
• Regression
• Classification
• Clustering
• 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?

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

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

Apache Spark is an open-source distributed computing system that’s designed for big data processing and analytics. It provides an interface for programming entire clusters with implicit data parallelism and fault tolerance. Here are some key concepts to understand:

1. Resilient Distributed Datasets (RDDs): RDDs are the fundamental data structure in Spark. They represent distributed collections of objects across a cluster and can be operated on in parallel. RDDs are immutable, meaning you can’t change them once they’re created, but you can transform them into new RDDs through operations like map, filter, reduce, etc.
2. Transformations and Actions: In Spark, transformations are operations that produce a new RDD from an existing one (like map, filter, flatMap, etc.), while actions are operations that trigger computation and return results (like collect, count, reduce, saveAsTextFile, etc.). Transformations are lazy, meaning they don’t execute immediately; they only execute when an action is called.
3. Spark Context: Spark Context (sc) is the entry point to any Spark functionality. It represents the connection to a Spark cluster and can be used to create RDDs, broadcast variables, and accumulators, as well as to perform various operations on RDDs.
4. DataFrames and Datasets: DataFrames and Datasets are higher-level abstractions introduced in Spark 2.0 for working with structured and semi-structured data. They provide a more intuitive API compared to RDDs and offer optimizations under the hood through Spark’s Catalyst optimizer.
5. Spark SQL: Spark SQL is a component of Spark for structured data processing. It allows you to run SQL queries and Spark SQL functions on Spark data structures, including RDDs, DataFrames, and Datasets. It’s widely used for data manipulation, aggregation, and analysis.
6. Cluster Managers: Spark can run on various cluster managers like Apache Mesos, Hadoop YARN, or Spark’s own built-in cluster manager. These managers allocate resources across applications in a shared or isolated environment.
7. Spark Streaming: Spark Streaming is an extension of the core Spark API that enables scalable, high-throughput, fault-tolerant stream processing of live data streams. It allows you to process real-time data using the same programming model as batch processing.
8. Machine Learning with Spark MLlib: Spark MLlib is Spark’s scalable machine learning library. It provides a wide array of machine learning algorithms and tools for building, training, and deploying machine learning models at scale.

These are just some of the fundamental concepts in Apache Spark. It’s a powerful framework with a wide range of capabilities for processing and analyzing large-scale data efficiently.