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What is the most performant out-of-the-box solution in Anypoint Platform to track transaction state in an asynchronously executing long-running process implemented as a Mule application deployed to multiple CloudHub workers?
Correct Answe r: Persistent Object Store
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>> Redis distributed cache is performant but NOT out-of-the-box solution in Anypoint Platform
>> File-storage is neither performant nor out-of-the-box solution in Anypoint Platform
>> java.util.WeakHashMap needs a completely custom implementation of cache from scratch using Java code and is limited to the JVM where it is running. Which means the state in the cache is not worker aware when running on multiple workers. This type of cache is local to the worker. So, this is neither out-of-the-box nor worker-aware among multiple workers on cloudhub. https://www.baeldung.com/java-weakhashmap
>> Persistent Object Store is an out-of-the-box solution provided by Anypoint Platform which is performant as well as worker aware among multiple workers running on CloudHub. https://docs.mulesoft.com/object-store/
So, Persistent Object Store is the right answer.
An API with multiple API implementations (Mule applications) is deployed to both CloudHub and customer-hosted Mule runtimes. All the deployments are managed by
the MuleSoft-hosted control plane. An alert needs to be triggered whenever an API implementation stops responding to API requests, even if no API clients have called the API implementation for some time.
What is the most effective out-of-the-box solution to create these alerts to monitor the API implementations?
In scenarios where multiple API implementations are deployed across different environments (CloudHub and customer-hosted runtimes), Anypoint Functional Monitoring is the most effective tool to monitor API availability and trigger alerts when an API implementation becomes unresponsive. Here's how it works:
Using Anypoint Functional Monitoring:
Functional Monitoring allows you to create monitors that periodically invoke specific endpoints on the API implementations, simulating a client request. This helps ensure that the API is responsive, even if no actual client requests are being made.
If an API implementation does not respond as expected, Functional Monitoring can generate alerts, notifying administrators of potential issues.
Why Option A is Correct:
By setting up Functional Monitoring to automatically invoke the API endpoints at regular intervals, you ensure continuous monitoring and alerting capabilities, which are especially useful for APIs that may experience periods of low or no traffic. This approach provides a proactive solution, allowing you to identify and address issues before actual users are impacted.
of Incorrect Options:
Option B suggests modifying client applications to trigger alerts, which is not a best practice as it shifts monitoring responsibility to clients, reducing control and consistency.
Option C involves handling exceptions within client applications, which does not address situations where no clients are making requests.
Option D proposes a Worker Not Responding alert in Runtime Manager, which is limited to worker-specific alerts and may not reliably monitor the API's actual responsiveness to requests.
Reference For further information, refer to MuleSoft documentation on Anypoint Functional Monitoring setup and usage for API availability monitoring.
Which of the following best fits the definition of API-led connectivity?
Correct Answe r: API-led connectivity is not just an architecture or technology but also a way to organize people and processes for efficient IT delivery in the organization.
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The application network is recomposable: it is built for change because it "bends but does not break"
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>> Application Network is a disposable architecture.
>> Which means, it can be altered without disturbing entire architecture and its components.
>> It bends as per requirements or design changes but does not break
Due to a limitation in the backend system, a system API can only handle up to 500 requests per second. What is the best type of API policy to apply to the system API to avoid overloading the backend system?
Correct Answe r: Spike control
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>> First things first, HTTP Caching policy is for purposes different than avoiding the backend system from overloading. So this is OUT.
>> Rate Limiting and Throttling/ Spike Control policies are designed to limit API access, but have different intentions.
>> Rate limiting protects an API by applying a hard limit on its access.
>> Throttling/ Spike Control shapes API access by smoothing spikes in traffic.
That is why, Spike Control is the right option.
