A mule application must periodically process a large dataset which varies from 6 GB lo 8 GB from a back-end database and write transform data lo an FTPS server using a properly configured bad job scope.
The performance requirements of an application are approved to run in the cloud hub 0.2 vCore with 8 GB storage capacity and currency requirements are met.
How can the high rate of records be effectively managed in this application?
To implement predictive maintenance on its machinery equipment, ACME Tractors has installed thousands of IoT sensors that will send data for each machinery asset as sequences of JMS messages, in near real-time, to a JMS queue named SENSOR_DATA on a JMS server. The Mule application contains a JMS Listener operation configured to receive incoming messages from the JMS servers SENSOR_DATA JMS queue. The Mule application persists each received JMS message, then sends a transformed version of the corresponding Mule event to the machinery equipment back-end systems.
The Mule application will be deployed to a multi-node, customer-hosted Mule runtime cluster. Under normal conditions, each JMS message should be processed exactly once.
How should the JMS Listener be configured to maximize performance and concurrent message processing of the JMS queue?
As an enterprise architect, what are the two reasons for which you would use a canonical data model in the new integration project using Mulesoft Anypoint platform ( choose two answers )
A new upstream API Is being designed to offer an SLA of 500 ms median and 800 ms maximum (99th percentile) response time. The corresponding API implementation needs to sequentially invoke 3 downstream APIs of very similar complexity. The first of these downstream APIs offers the following SLA for its response time: median: 100 ms, 80th percentile: 500 ms, 95th percentile: 1000 ms. If possible, how can a timeout be set in the upstream API for the invocation of the first downstream API to meet the new upstream API's desired SLA?
A company is using Mulesoft to develop API's and deploy them to Cloudhub and on premises targets. Recently it has decided to enable Runtime Fabric deployment option as well and infrastructure is set up for this option.
What can be used to deploy Runtime Fabric?
An API implementation is being designed that must invoke an Order API which is known to repeatedly experience downtime. For this reason a fallback API is to be called when the Order API is unavailable. What approach to designing invocation of the fallback API provides the best resilience?
A stock broking company makes use of CloudHub VPC to deploy Mule applications. Mule application needs to connect to a database application in the customers on-premises corporate data center and also to a Kafka cluster running in AWS VPC.
How is access enabled for the API to connect to the database application and Kafka cluster securely?
An organization is using Mulesoft cloudhub and develops API's in the latest version. As a part of requirements for one of the API's, third party API needs to be called. The security team has made it clear that calling any external API needs to have include listing
As an integration architect please suggest the best way to accomplish the design plan to support these requirements?
A mule application designed to fulfil two requirements
a) Processing files are synchronously from an FTPS server to a back-end database using VM intermediary queues for load balancing VM events
b) Processing a medium rate of records from a source to a target system using batch job scope
Considering the processing reliability requirements for FTPS files, how should VM queues be configured for processing files as well as for the batch job scope if the application is deployed to Cloudhub workers?
How are the API implementation , API client, and API consumer combined to invoke and process an API ?
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