AWS Certified Big Data – Specialty — Question 12

A game company needs to properly scale its game application, which is backed by DynamoDB. Amazon
Redshift has the past two years of historical data. Game traffic varies throughout the year based on various factors such as season, movie release, and holiday season. An administrator needs to calculate how much read and write throughput should be provisioned for DynamoDB table for each week in advance.
How should the administrator accomplish this task?

Answer options

• A. Feed the data into Amazon Machine Learning and build a regression model.
• B. Feed the data into Spark Mlib and build a random forest modest.
• C. Feed the data into Apache Mahout and build a multi-classification model.
• D. Feed the data into Amazon Machine Learning and build a binary classification model.

Correct answer: B

Explanation

The correct answer is B because using Spark Mlib to build a random forest model is suitable for predicting varying game traffic based on historical data. The other options either utilize incorrect machine learning models or frameworks that do not align with the needs of the use case, such as regression or classification models that wouldn't effectively handle the traffic forecasting required.