Your company produces customer commissioned one-of-a-kind skiing helmets combining nigh fashion with custom technical enhancements Customers can show off their Individuality on the ski slopes and have access to head-up-displays. GPS rear-view cams and any other technical innovation they wish to embed in the helmet. The current manufacturing process is data rich and complex including assessments to ensure that the custom electronics and materials used to assemble the helmets are to the highest standards Assessments are a mixture of human and automated assessments you need to add a new set of assessment to model the failure modes of the custom electronics using GPUs with CUDA, across a cluster of servers with low latency networking. What architecture would allow you to automate the existing

Question

process using a hybrid approach and ensure that the architecture can support the evolution of processes over time?

A. Use AWS Data Pipeline to manage movement of data & meta-data and assessments Use an auto-scaling group of G2 instances in a placement group.
B. Use Amazon Simple Workflow (SWF) to manages assessments, movement of data & meta-data Use an auto-scaling group of G2 instances in a placement group.
C. Use Amazon Simple Workflow (SWF) to manages assessments movement of data & meta-data Use an auto-scaling group of C3 instances with SR-IOV (Single Root I/O Virtualization).
D. Use AWS data Pipeline to manage movement of data & meta-data and assessments use auto-scaling group of C3 with SR-IOV (Single Root I/O virtualization).

Answer 1

Answer: B. Use Amazon Simple Workflow (SWF) to manages assessments, movement of data & meta-data Use an auto-scaling group of G2 instances in a placement group.