AWS Certified Big Data – Specialty — Question 29
A city has been collecting data on its public bicycle share program for the past three years. The 5PB dataset currently resides on Amazon S3. The data contains the following datapoints:
✑ Bicycle origination points
✑ Bicycle destination points
✑ Mileage between the points
✑ Number of bicycle slots available at the station (which is variable based on the station location)
✑ Number of slots available and taken at a given time
The program has received additional funds to increase the number of bicycle stations available. All data is regularly archived to Amazon Glacier.
The new bicycle stations must be located to provide the most riders access to bicycles.
How should this task be performed?
Answer options
- A. Move the data from Amazon S3 into Amazon EBS-backed volumes and use an EC-2 based Hadoop cluster with spot instances to run a Spark job that performs a stochastic gradient descent optimization.
- B. Use the Amazon Redshift COPY command to move the data from Amazon S3 into Redshift and perform a SQL query that outputs the most popular bicycle stations.
- C. Persist the data on Amazon S3 and use a transient EMR cluster with spot instances to run a Spark streaming job that will move the data into Amazon Kinesis.
- D. Keep the data on Amazon S3 and use an Amazon EMR-based Hadoop cluster with spot instances to run a Spark job that performs a stochastic gradient descent optimization over EMRFS.
Correct answer: B
Explanation
The correct answer is B because using Amazon Redshift allows for efficient querying of large datasets, making it suitable for identifying the most popular bicycle stations. Option A is incorrect as moving data to EBS and using Hadoop may not be the most efficient method for this specific task. Option C is not suitable as transferring data into Kinesis does not directly address the need for analyzing station popularity. Option D, while using EMR, still focuses on optimization rather than directly identifying popular stations through SQL queries.