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

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A System API is designed to retrieve data from a backend system that has scalability challenges. What API policy can best safeguard the backend system?

• A.SLA-based rate limiting
• B.Client ID enforcement
• C.Auth 2 token enforcement
• D.IPwhitelist

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Refer to the exhibit.

An organization uses one specific CloudHub (AWS) region for all CloudHub deployments.
How are CloudHub workers assigned to availability zones (AZs) when the organization's Mule applications are deployed to CloudHub in that region?

• A.Workers belonging to a given environment are assigned to the same AZ within that region
• B.An AZ is randomly selected for a Mule application, and all the Mule application's CloudHub workers are assigned to that one AZ
• C.Workers are randomly distributed across available AZs within that region
• D.AZs are selected as part of the Mule application's deployment configuration

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How are an API implementation, API client, and API consumer combined to invoke and process an API?

• A.The ApI consumer creates an API client, which sends API invocations to an API such that they are processed by an API implementation
• B.The API client creates an API consumer, which receives API invocations from an API such that they are processed for an API implementation
• C.The ApI client creates an API consumer, which sends API invocations to an API such that they are processed by an API implementation
• D.The API consumer creates an API implementation, which receives API invocations from an API such that they are processed for an API client

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When designing an upstream API and its implementation, the development team has been advised to NOT set timeouts when invoking a downstream API, because that downstream API has no SLA that can be relied upon.
This is the only downstream API dependency of that upstream API.
Assume the downstream API runs uninterrupted without crashing. What is the impact of this advice?

• A.An SLA for the upstream API CANNOT be provided
• B.A default timeout of 500 ms will automatically be applied by the Mule runtime in which the upstream API implementation executes
• C.The invocation of the downstream API will run to completion without timing out
• D.A toad-dependent timeout of less than 1000 ms will be applied by the Mule runtime in which the downstream API implementation executes

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