A Mule application uses the Database connector.
What condition can the Mule application automatically adjust to or recover from without needing to restart or
redeploy the Mule application?
A DevOps team has adequate observability of individual system behavior and performance, but it struggles to
track the entire lifecycle of each request across different microservices.
Which additional observability approach should this team consider adopting?
An Order microservice and a Fulfillment microservice are being designed to communicate with their dients
through message-based integration (and NOT through API invocations).
The Order microservice publishes an Order message (a kind of command message) containing the details of an
order to be fulfilled. The intention is that Order messages are only consumed by one Mute application, the
Fulfillment microservice.
The Fulfilment microservice consumes Order messages, fulfills the order described therein, and then publishes
an OrderFulfilted message (a kind of event message). Each OrderFulfilted message can be consumed by any
interested Mule application, and the Order microservice is one such Mute application.
What is the most appropriate choice of message broker(s) and message destination(s) in this scenario?
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?
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