Cognitive Class: Spark Fundamentals I Exam Answers

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In this course, you learn the fundamentals of Spark, the technology that is revolutionizing the analytics and big data world! Spark is an open-source processing engine built around speed, ease of use, and analytics. If you have large amounts of data that require low latency processing that a typical MapReduce program cannot provide, Spark is the way to go.

Organization	Cognitive Class
Trainer	Henry L. Quach, Alan Barnes
Level	Beginner
Duration	5hr
Language	English
Price	Free
Certificate	Yes
Spark Fundamentals I	Click Here

Cognitive Class – Spark Fundamentals I Answers

Module 1: Introduction to Spark

1. What gives Spark its speed advantage for complex applications?

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

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

1. Analyzing data to obtain insights
2. Programming with Spark’s API
3. Transforming data into a useable form for analysis
4. Developing a data processing system
5. Tuning an application for a business use case

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

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

Module 2: Resilient Distributed Dataset and DataFrames

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

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

2. What happens when an action is executed?

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

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

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

Module 3: Spark application programming

1. What is SparkContext?

1. An object that represents the connection to a Spark cluster
2. A tool for linking to nodes
3. A tool that provides fault tolerance
4. The built-in shell for the Spark engine
5. A programming language for applications

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

1. Transformations and actions
2. Passing by reference
3. Static methods in a global singleton
4. Import statements
5. Anonymous function syntax

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

1. SparkContext object
2. Transformations and actions
3. Business Logic
4. Import statements
5. All of the above

Module 4: Introduction to the Spark libraries

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

1. Hive
2. MLlib
3. Spark Streaming
4. Spark SQL
5. GraphX

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

1. Kafka
2. JSON
3. Parquet
4. HDFS
5. Hive

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

1. True
2. False

Module 5: Spark configuration, monitoring and tuning

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

1. Driver Program
2. SparkContext
3. Cluster Manager
4. Worker node
5. Cache

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

1. The SparkConf object
2. The Spark Shell
3. Executor Processes
4. Environment variables
5. Logging properties

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

1. Scheduler Configuration
2. Memory Tuning
3. Data Serialization
4. Using Broadcast variables
5. Using nested structures

Spark Fundamentals I Final Exam Answers

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

1. Parallelization
2. Action
3. Persistence
4. Transformation
5. Evaluation

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

1. True
2. False

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

1. Count
2. Maximum
3. Minimum
4. Average
5. All of the above operations will work properly

4. Spark supports which of the following libraries?

1. GraphX
2. Spark Streaming
3. MLlib
4. Spark SQL
5. All of the above

5. Spark supports which of the following programming languages?

1. C++ and Python
2. Scala, Java, C++, Python, Perl
3. Scala, Perl, Java
4. Scala, Python, Java, R
5. Java and Scala

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

1. True
2. False

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

1. MEMORY_AND_DISK_SER
2. MEMORY_AND_DISK
3. MEMORY_ONLY_SER
4. MEMORY_ONLY

8. Which of the following statements does NOT describe accumulators?

1. They can only be read by the driver
2. Programmers can extend them beyond numeric types
3. They implement counters and sums
4. They can only be added through an associative operation
5. They are read-only

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

1. True
2. False

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

1. True
2. False

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

1. True
2. False

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

1. –class
2. –master
3. –deploy-mode
4. –conf
5. None of the above

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

1. YARN
2. Helix
3. Mesos
4. Spark
5. All of the above are supported

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

1. HiveQL only
2. Scala, SQL, and HiveQL
3. Scala and SQL
4. Scala and HiveQL
5. SQL only

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

1. True
2. False

16. The MLlib library contains which of the following algorithms?

1. Dimensionality Reduction
2. Regression
3. Classification
4. Clustering
5. All of the above

17. What is the purpose of the GraphX library?

1. To create a visual representation of the data
2. To generate data-parallel models
3. To create a visual representation of a directed acyclic graph (DAG)
4. To perform graph-parallel computations
5. To convert from data-parallel to graph-parallel algorithms

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

1. Properties set on SparkConf, values in spark-defaults.conf, flags passed to spark-submit
2. Flags passed to spark-submit, values in spark-defaults.conf, properties set on SparkConf
3. Values in spark-defaults.conf, properties set on SparkConf, flags passed to spark-submit
4. Values in spark-defaults.conf, flags passed to spark-submit, properties set on SparkConf
5. Properties set on SparkConf, flags passed to spark-submit, values in spark-defaults.conf

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

1. True
2. False

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

1. Apache Avro
2. Java Serialization
3. Protocol Buffers
4. Kyro Serialization
5. TPL

Wrap Up

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