NPTEL Big Data Computing Assignment 3 Answer
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NPTEL Big Data Computing Assignment
In today’s fast-paced digital world , the incredible amount of data being generated every minute has grown tremendously from sensors used to gather climate information, posts to social media sites, digital pictures and videos, purchase transaction records, and GPS signals from cell phone to name a few. This amount of large data with different velocities and varieties is termed as big data and its analytics enables professionals to convert extensive data through statistical and quantitative analysis into powerful insights that can drive efficient decisions. This course provides an in-depth understanding of terminologies and the core concepts behind big data problems, applications, systems and the techniques, that underlie today’s big data computing technologies. It provides an introduction to some of the most common frameworks such as Apache Spark, Hadoop, MapReduce, Large scale data storage technologies such as in-memory key/value storage systems, NoSQL distributed databases, Apache Cassandra, HBase and Big Data Streaming Platforms such as Apache Spark Streaming, Apache Kafka Streams that has made big data analysis easier and more accessible. And while discussing the concepts and techniques, we will also look at various applications of Big Data Analytics using Machine Learning, Deep Learning, Graph Processing and many others. The course is suitable for all UG/PG students and practicing engineers/ scientists from the diverse fields and interested in learning about the novel cutting edge techniques and applications of Big Data Computing.
This course can have Associate in Nursing unproctored programming communication conjointly excluding the Proctored communication, please check announcement section for date and time. The programming communication can have a weightage of twenty fifth towards the ultimate score.
- Assignment score = 25% of average of best 8 assignments out of the total 12 assignments given in the course.
- ( All assignments in a particular week will be counted towards final scoring – quizzes and programming assignments).
- Unproctored programming exam score = 25% of the average scores obtained as part of Unproctored programming exam – out of 100
- Proctored Exam score =50% of the proctored certification exam score out of 100
UNPROCTORED PROGRAMMING EXAM SCORE >=10/25 AND PROCTORED EXAM SCORE >= 20/50.
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1. Consider the following statements in the context of Spark:
Statement 1: Spark improves efficiency through in-memory computing primitives and general computation graphs.
Statement 2: Spark improves usability through high-level APIs in Java, Scala, Python and also provides an interactive shell.
a. Only statement 1 is true
b. Only statement 2 is true
c. Both statements are true
d. Both statements are false
Answer:- c
2. True or False?
Resilient Distributed Datasets (RDDs) are fault-tolerant and immutable.
a. True
b. False
Answer:- a
NPTEL Big Data Computing Assignment 2 Answers 2022
3. In Spark, a ______________________is a read-only collection of objects partitioned across a set of machines that can be rebuilt if a partition is lost.
a. Spark Streaming
b. FlatMap
c. Resilient Distributed Dataset (RDD)
d. Driver
Answer:- c
4. Given the following definition about the join transformation in Apache Spark:
Answer:- b
5. True or False ?
Apache Spark potentially run batch-processing programs up to 100 times faster than Hadoop MapReduce in memory, or 10 times faster on disk.
a. True
b. False
Answer:- a
6. ______________ leverages Spark Core fast scheduling capability to perform streaming analytics.
a. MLlib
b. GraphX
c. RDDs
d. Spark Streaming
Answer:- d
NPTEL Big Data Computing Assignment 2 Answers 2022
7. ____________________ is a distributed graph processing framework on top of Spark.
a. GraphX
b. MLlib
c. Spark streaming
d. All of the mentioned
Answer:- a
8. Which of the following are the simplest NoSQL databases ?
a. Wide-column
b. Key-value
c. Document
d. All of the mentioned
Answer:- b
9. Consider the following statements:
Statement 1: Scale out means grow your cluster capacity by replacing with more powerful machines.
Statement 2: Scale up means incrementally grow your cluster capacity by adding more COTS machines (Components Off the Shelf).
a. Only statement 1 is true
b. Only statement 2 is true
c. Both statements are false
d. Both statements are true
Answer:- For Answer Click Here
10. Point out the incorrect statement in the context of Cassandra:
a. It is originally designed at Facebook
b. It is designed to handle large amounts of data across many commodity servers, providing high availability with no single point of failure
c. It is a centralized key-value store
d. It uses a ring-based DHT (Distributed Hash Table) but without finger tables or routing
Answer:- c
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