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NEW QUESTION # 49
What is a key difference between synchronous and asynchronous logging from Mule applications?
Answer: D
Explanation:
Types of logging:
A) Synchronous: The execution of thread that is processing messages is interrupted to wait for the log message to be fully handled before it can continue.
* The execution of the thread that is processing your message is interrupted to wait for the log message to be fully output before it can continue
* Performance degrades because of synchronous logging
* Used when the log is used as an audit trail or when logging ERROR/CRITICAL messages
* If the logger fails to write to disk, the exception would raise on the same thread that's currently processing the Mule event. If logging is critical for you, then you can rollback the transaction.
B) Asynchronous:
* The logging operation occurs in a separate thread, so the actual processing of your message won't be delayed to wait for the logging to complete
* Substantial improvement in throughput and latency of message processing
* Mule runtime engine (Mule) 4 uses Log4j 2 asynchronous logging by default
* The disadvantage of asynchronous logging is error handling.
* If the logger fails to write to disk, the thread doing the processing won't be aware of any issues writing to the disk, so you won't be able to rollback anything. Because the actual writing of the log gets differed, there's a chance that log messages might never make it to disk and get lost, if Mule were to crash before the buffers are flushed.
------------------------------------------------------------------------------------------------------------------ So Correct answer is: Asynchronous logging can improve Mule event processing throughput while also reducing the processing time for each Mule event
NEW QUESTION # 50
An organization has implemented a continuous integration (CI) lifecycle that promotes Mule applications through code, build, and test stages. To standardize the organization's CI journey, a new dependency control approach is being designed to store artifacts that include information such as dependencies, versioning, and build promotions.
To implement these process improvements, the organization will now require developers to maintain all dependencies related to Mule application code in a shared location.
What is the most idiomatic (used for its intended purpose) type of system the organization should use in a shared location to standardize all dependencies related to Mule application code?
Answer: C
NEW QUESTION # 51
A manufacturing company plans to deploy Mule applications to its own
Azure Kubernetes service infrastructure.The organization wants to make
the Mule applications more available and robust by deploying each Mule
application to an isolated Mule runtime in a Docker container while
managing all the Mule applications from the MuleSoft-hosted control plane.
What choice of runtime plane meets these organizational requirements?
Answer: C
Explanation:
Anypoint Runtime Fabric is the appropriate choice for deploying Mule applications in an isolated Mule runtime within Docker containers on an Azure Kubernetes Service (AKS) infrastructure. This solution provides a containerized and orchestrated environment managed from the MuleSoft-hosted control plane. It enhances the availability, scalability, and robustness of Mule applications by allowing fine-grained control over deployments and providing built-in support for high availability and fault tolerance.
References:
* Anypoint Runtime Fabric Overview
* Deploying Mule Applications to Kubernetes
NEW QUESTION # 52
Refer to the exhibit.
A Mule application is deployed to a cluster of two customer-hosted Mute runtimes. The Mute application has a flow that polls a database and another flow with an HTTP Listener.
HTTP clients send HTTP requests directly to individual cluster nodes.
What happens to database polling and HTTP request handling in the time after the primary (master) node of the cluster has railed, but before that node is restarted?
Answer: A
Explanation:
Correct answer is Database polling continues Only HTTP requests sent to the remaining node continue to be accepted. Explanation : Architecture descripted in the question could be described as follows.When node 1 is down , DB polling will still continue via node 2 . Also requests which are coming directly to node 2 will also be accepted and processed in BAU fashion. Only thing that wont work is when requests are sent to Node 1 HTTP connector. The flaw with this architecture is HTTP clients are sending HTTP requests directly to individual cluster nodes. By default, clustering Mule runtime engines ensures high system availability. If a Mule runtime engine node becomes unavailable due to failure or planned downtime, another node in the cluster can assume the workload and continue to process existing events and messages Diagram Description automatically generated
NEW QUESTION # 53
A project team uses RAML specifications to document API functional requirements and deliver API definitions. As per the current legal requirement, all designed API definitions to be augmented with an additional non-functional requirement to protect the services from a high rate of requests according to define service level agreements.
Assuming that the project is following Mulesoft API governance and policies, how should the project team convey the necessary non-functional requirement to stakeholders?
Answer: C
Explanation:
To ensure that non-functional requirements, such as rate limiting, are clearly communicated and enforced in the designed API definitions, the project team should use API fragments for the appropriate policy. Here's why option D is correct:
* API Governance and Policies: Mulesoft's API governance framework allows the definition and enforcement of policies across APIs to ensure consistency and compliance with organizational standards. These policies can include security, rate limiting, logging, and more.
* Policy Fragments: By updating API definitions with policy fragments, the team can encapsulate the non-functional requirements within the API specification itself. This approach ensures that these requirements are an integral part of the API design and are automatically applied whenever the API is deployed.
* Publishing to Exchange: Publishing the updated API definitions with the policy fragments to Anypoint Exchange makes them available for reuse and ensures that all stakeholders have access to the latest, compliant API specifications.
Example of adding a rate limiting policy fragment to a RAML file:
#%RAML 1.0 title: Example API version: v1 baseUri: https://api.example.com/v1 ... /* Include the rate limiting policy fragment */ uses: rateLimitPolicy: !include rate-limit-policy.raml The rate-limit-policy.raml fragment might define the specific rate limiting rules as per the service level agreements.
References
* MuleSoft API Manager
* Defining and Using API Fragments
NEW QUESTION # 54
......
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