Introduction to Big Data Week 2 Quiz Answers

Introduction to Big Data complete course is currently being offered by UC San Diego through Coursera platform.

Learning Outcomes for Introduction to Big Data Course!

At the end of this course, you will be able to:

* Describe the Big Data landscape including examples of real world big data problems including the three key sources of Big Data: people, organizations, and sensors. 

* Explain the V’s of Big Data (volume, velocity, variety, veracity, valence, and value) and why each impacts data collection, monitoring, storage, analysis and reporting.

* Get value out of Big Data by using a 5-step process to structure your analysis. 

* Identify what are and what are not big data problems and be able to recast big data problems as data science questions.

* Provide an explanation of the architectural components and programming models used for scalable big data analysis.

* Summarize the features and value of core Hadoop stack components including the YARN resource and job management system, the HDFS file system and the MapReduce programming model.

Instructors for Introduction to Big Data Course!

– Ilkay Altintas

– Amarnath Gupta

Skills You Will Gain

• Big Data
• Apache Hadoop
• Mapreduce
• Cloudera

Also Check: How to Apply for Coursera Financial Aid

V for the V’s of Big Data

Q1) Amazon has been collecting review data for a particular product. They have realized that almost 90% of the reviews were mostly a 5/5 rating. However, of the 90%, they realized that 50% of them were customers who did not have proof of purchase or customers who did not post serious reviews about the product. Of the following, which is true about the review data collected in this situation?

Q2) As mentioned in the slides, what are the challenges to data with a high valence?

• Complex Data Exploration Algorithms

Q3) Which of the following are the 6 V’s in big data?

Q4) What is the veracity of big data?

• The connectedness of data.

• The speed at which data is produced.

• The abnormality or uncertainties of data.

Q5) What are the challenges of data with high variety?

• The quality of data is low.

• Hard in utilizing group event detection.

• Hard to perform emergent behavior analysis.

Q6) Which of the following is the best way to describe why it is crucial to process data in real-time?

• Prevents missed opportunities.

• More expensive to batch process.

• Batch processing is an older method that is not as accurate as real-time processing.

Q7) What are the challenges with big data that has high volume?

• Storage and Accessibility

• Speed Increase in Processing

• Cost, Scalability, and Performance