MCPA-Level-1 Exam Dumps : MuleSoft Certified Platform Architect - Level 1

Explanation

Correct Answer: Anypoint Runtime Fabric

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>> When a customer is already having an Azure environment, It is not at all an ideal approach to go with hybrid model having some Mule Runtimes hosted on Azure and some on MuleSoft. This is unnecessary and useless.

>> CloudHub is a Mulesoft-hosted Runtime plane and is on AWS. We cannot customize to point CloudHub to customer's Azure environment.

>> Anypoint Platform for Pivotal Cloud Foundry is specifically for infrastructure provided by Pivotal Cloud Foundry

>> Anypoint Runtime Fabric is right answer as it is a container service that automates the deployment and orchestration of Mule applications and API gateways. Runtime Fabric runs within a customer-managed infrastructure on AWS, Azure, virtual machines (VMs), and bare-metal servers.

-Some of the capabilities of Anypoint Runtime Fabric include:

-Isolation between applications by running a separate Mule runtime per application.

-Ability to run multiple versions of Mule runtime on the same set of resources.

-Scaling applications across multiple replicas.

-Automated application fail-over.

-Application management with Anypoint Runtime Manager.

Explanation

Correct Answer: SLA-based rate limiting

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>> Client Id enforement policy is a "Compliance" related NFR and does not help in maintaining the "Quality of Service (QoS)". It CANNOT and NOT meant for protecting the backend systems from scalability challenges.

>> IP Whitelisting and OAuth 2.0 token enforcement are "Security" related NFRs and again does not help in maintaining the "Quality of Service (QoS)". They CANNOT and are NOT meant for protecting the backend systems from scalability challenges.

Rate Limiting, Rate Limiting-SLA, Throttling, Spike Control are the policies that are "Quality of Service (QOS)" related NFRs and are meant to help in protecting the backend systems from getting overloaded.

Explanation

Correct Answer: The CloudHub shared load balancer

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The scenario in this question can be split as below:

>> There are 3 CloudHub workers (So, there are already good number of workers to handle high volume of requests)

>> The workers are not using static IP addresses (So, one CANNOT use customer load-balancing solutions without static IPs)

>> Looking for most cost-effective component to load balance the client requests among the workers.

Based on the above details given in the scenario:

>> Runtime autoscaling is NOT at all cost-effective as it incurs extra cost. Most over, there are already 3 workers running which is a good number.

>> We cannot go for a customer-hosted load balancer as it is also NOT most cost-effective (needs custom load balancer to maintain and licensing) and same time the Mule App is not having Static IP Addresses which limits from going with custom load balancing.

>> An API Proxy is irrelevant there as it has no role to play w.r.t handling high volumes or load balancing.

So, the only right option to go with and fits the purpose of scenario being most cost-effective is - using a CloudHub Shared Load Balancer.