What correctly characterizes unit tests of Mule applications?

• A. They must be run in a unit testing environment with dedicated Mule runtimes for the environment
• B. They are typically written using MUnit to run in an embedded Mule runtime that does not require external connectivity
• C. They must be triggered by an external client tool or event source
• D. They test the validity of input and output of source and target systems

Several times a week, an API implementation shows several thousand requests per minute in an Anypoint Monitoring dashboard, Between these bursts, the dashboard shows between two and five requests per minute. The API implementation is running on Anypoint Runtime Fabric with two non-clustered replicas, reserved vCPU 1.0 and vCPU Limit 2.0.
An API consumer has complained about slow response time, and the dashboard shows the 99 percentile is greater than 120 seconds at the time of the complaint. It also shows greater than 90% CPU usage during these time periods.
In manual tests in the QA environment, the API consumer has consistently reproduced the slow response time and high CPU usage, and there were no other API requests at this time. In a brainstorming session, the engineering team has created several proposals to reduce the response time for requests.
Which proposal should be pursued first?

• A. Increase the vCPU resources of the API implementation
• B. Increase the number of replicas of the API implementation
• C. Modify the API client to split the problematic request into smaller, less-demanding requests
• D. Throttle the APT client to reduce the number of requests per minute

A retail company is using an Order API to accept new orders. The Order API uses a JMS queue to submit orders to a backend order management service. The normal load for orders is being handled using two (2) CloudHub workers, each configured with 0.2 vCore. The CPU load of each CloudHub worker normally runs well below 70%. However, several times during the year the Order API gets four times (4x) the average number of orders. This causes the CloudHub worker CPU load to exceed 90% and the order submission time to exceed 30 seconds. The cause, however, is NOT the backend order management service, which still responds fast enough to meet the response SLA for the Order API. What is the MOST resource-efficient way to configure the Mule application's CloudHub deployment to help the company cope with this performance challenge?

• A. Permanently increase the number of CloudHub workers by four times (4x) to eight (8) CloudHub workers
• B. Use a horizontal CloudHub autoscaling policy that triggers on CPU utilization greater than 70%
• C. Permanently increase the size of each of the two (2) CloudHub workers by at least four times (4x) to one (1) vCore
• D. Use a vertical CloudHub autoscaling policy that triggers on CPU utilization greater than 70%

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. No timeout is possible to meet the upstream API's desired SLA; a different SLA must be negotiated with the first downstream API or invoke an alternative API
• B. Set a timeout of 50 ms; this times out more invocations of that API but gives additional room for retries
• C. Set a timeout of 100 ms; that leaves 400 ms for the other two downstream APIs to complete
• D. Do not set a timeout; the Invocation of this API Is mandatory and so we must wait until it responds