Your company is planning on hosting a set of EMR clusters for the purposes of Machine Learning and ad-hoc query analysis.
All the data would be stored on Amazon S3
The underlying instance types needs to be setup for the clusters.
Which of the following would you recommend?
Click on the arrows to vote for the correct answer
A. B. C. D.Answer - D.
The C type Instances are good for Machine Learning purposes.
High performance web servers, scientific modelling, batch processing, distributed analytics, high-performance computing (HPC), machine/deep learning inference, ad serving, highly scalable multiplayer gaming, and video encoding.
The R Instance types are good for ad-hoc work since they have the capacity for burstable performances.
For more information on EC2 Instance types, please refer to the below URL.
https://aws.amazon.com/ec2/instance-types/When setting up EMR clusters for Machine Learning and ad-hoc query analysis, it is important to select appropriate instance types based on the specific requirements of each workload.
Instance types are classified based on various factors like CPU, memory, storage, network capacity, and cost. The recommended instance types for EMR clusters depend on the specific workload requirements.
In this scenario, since all the data is stored on Amazon S3, the instance types need to be optimized for processing large datasets.
Option A recommends T for Machine Learning clusters and R for ad-hoc query analysis.
T instances are optimized for burstable workloads and provide a balance of compute, memory, and network resources. They can handle machine learning workloads efficiently, especially those that require parallel processing. However, they may not be the best option for ad-hoc query analysis since they have lower memory capacity compared to other instance types.
R instances, on the other hand, are memory-optimized and provide high memory capacity, making them ideal for ad-hoc query analysis. However, they may not be optimal for machine learning workloads that require high processing power.
Option B recommends T for Machine Learning clusters and G2 for ad-hoc query analysis.
G2 instances are graphics-optimized instances that come with dedicated GPUs. They are typically used for graphics-intensive workloads and may not be the best option for ad-hoc query analysis.
Option C recommends M for Machine Learning clusters and I for ad-hoc query analysis.
M instances are general-purpose instances and provide a balance of compute, memory, and network resources. They are suitable for machine learning workloads that require moderate processing power and memory. I instances, on the other hand, are high I/O instances that provide high disk throughput, making them ideal for ad-hoc query analysis workloads that involve large amounts of data.
Option D recommends C for Machine Learning clusters and R for ad-hoc query analysis.
C instances are compute-optimized and provide high processing power, making them ideal for machine learning workloads that require intensive processing. R instances are memory-optimized, as mentioned earlier. While this combination may work well for machine learning workloads, it may not be the best option for ad-hoc query analysis.
Therefore, based on the workload requirements and instance characteristics, the recommended option would be C. for Machine Learning clusters and I. for ad-hoc query analysis. This combination provides high processing power and high I/O throughput, making it suitable for both machine learning and ad-hoc query analysis workloads.