You architect a solution that calculates 3D geometry from height-map data.
You have the following requirements:
-> Perform calculations in Azure.
-> Each node must communicate data to every other node.
-> Maximize the number of nodes to calculate multiple scenes as fast as possible.
-> Require the least amount of effort to implement.
You need to recommend a solution.
Which two actions should you recommend? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
Click on the arrows to vote for the correct answer
A. B. C. D. E.AC
The given requirements for the solution are:
To fulfill the given requirements, the following recommendations can be made:
A. Create a render farm that uses Azure Batch: Azure Batch is a cloud-based job scheduling and compute management service provided by Microsoft Azure. It allows the processing of large-scale parallel and high-performance computing (HPC) workloads. With Azure Batch, you can submit your jobs to a pool of compute nodes, and the service will automatically scale up or down the number of nodes based on the workload.
To use Azure Batch in this scenario, you can create a pool of compute nodes to perform the 3D geometry calculations. You can configure the nodes to communicate with each other using the Azure Batch service. The service will automatically scale up or down the number of nodes based on the workload, so you can maximize the number of nodes to calculate multiple scenes as fast as possible. Azure Batch is easy to implement, and you can use pre-built templates and tools to get started quickly.
D. Create a render farm that uses virtual machine (VM) scale sets: A VM scale set is an Azure compute resource that you can use to deploy and manage a set of identical VMs. VM scale sets are designed to support large-scale, compute-intensive workloads that require autoscaling. With VM scale sets, you can create a pool of VMs that can scale up or down based on the workload.
To use VM scale sets in this scenario, you can create a pool of VMs to perform the 3D geometry calculations. You can configure the VMs to communicate with each other using a load balancer. The load balancer will distribute the workload across the VMs, so you can maximize the number of nodes to calculate multiple scenes as fast as possible. VM scale sets are easy to implement and can be configured using pre-built templates and tools.
Therefore, the recommended solutions for this scenario are:
Note: Enabling parallel file systems on Azure or enabling parallel task execution on compute nodes may not be the most efficient way to fulfill the requirements of this scenario.