Your company produces customer commissioned one-of-a-kind skiing helmets, combining high
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 process using a hybrid approach, and
ensure that the architecture can support the evolution of processes over time.

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

B.
Use Amazon Simple Workflow (SWF) to manage assessments, movement of data & meta-data.
Use an auto-scaling group of C3 instances with SR-IOV (Single Root I/O Visualization).

C.
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 Visualization).

D.
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.