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| Section | Objectives |
|---|---|
| Performance and Scalability | - Scaling Mule applications - High availability design patterns |
| Anypoint Platform Architecture | - Runtime Fabric architecture overview - Deployment models (CloudHub, hybrid, on-premises) - Anypoint Platform components and capabilities |
| API Management and Governance | - API security policies and enforcement - API lifecycle management - Governance frameworks and best practices |
| Data Integration and Transformation | - Batch vs real-time integration patterns - Data mapping and transformation strategies |
| Security and Compliance | - Secure API exposure and threat protection - Authentication and authorization mechanisms |
| Integration Architecture Strategy | - System, process, and experience APIs - API-led connectivity principles - Designing enterprise integration architecture |
Mule-Arch-201試験問題は高品質であり、試験に簡単かつ正常に合格するのに役立ちます。 Mule-Arch-201試験の質問により、99％の合格率と高いヒット率が得られるため、Salesforce試験に合格できないことを心配する必要はありません。 当社のMule-Arch-201試験トレントは、専門家によって編集され、経験豊富な専門家によって承認され、理論と実践の開発状況に応じて更新されます。 当社のMule-Arch-201ガイドトレントは、試験をシミュレートしてタイミング機能を向上させることができます。
質問 # 77
A company is using an on-prem cluster in the data center as a runtime plane and MuleSoft-hosted control plane.
How can the company monitor the detailed performance metrics on the Mule applications deployed to the cluster from the control plane?
正解：B
解説：
Monitoring On-Premise Mule Applications:
For Mule applications deployed on an on-premises cluster, monitoring detailed performance metrics requires communication with the MuleSoft-hosted control plane. The control plane, when used with on-premises runtimes, relies on Anypoint Monitoring and requires a Monitoring Agent to gather and send detailed performance metrics.
Setting Up Monitoring:
To enable detailed metrics, the Monitoring Agent must be installed on each node in the cluster where Mule applications are deployed. This agent collects data on memory usage, CPU load, response times, and other metrics, and sends it to the control plane for aggregation and visualization.
Evaluating the Options:
Option A: Updating settings in the control plane alone does not enable detailed monitoring; the agent must be installed on each node to capture detailed metrics.
Option B (Correct Answer): Installing the Monitoring Agent on each node ensures that each runtime node in the cluster can send its metrics to the control plane, enabling detailed monitoring.
Option C: Installing the agent on a separate server would not be effective, as each node in the cluster needs to independently report its metrics to ensure full visibility.
Option D: The on-prem runtime does not automatically send detailed metrics to the control plane without the Monitoring Agent installed.
Conclusion:
Option B is the correct answer, as installing the Monitoring Agent on each node is essential for detailed performance monitoring of on-prem applications in a cluster.
Refer to MuleSoft's documentation on configuring Anypoint Monitoring for on-premises deployments and using the Monitoring Agent.
質問 # 78
An API client calls one method from an existing API implementation. The API implementation is later updated. What change to the API implementation would require the API client's invocation logic to also be updated?
正解：D
解説：
Correct Answe r: When a new required field is added to the method called by the API client
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>> Generally, the logic on API clients need to be updated when the API contract breaks.
>> When a new method or a child method is added to an API , the API client does not break as it can still continue to use its existing method. So these two options are out.
>> We are left for two more where "datatype of the response if changed" and "a new required field is added".
>> Changing the datatype of the response does break the API contract. However, the question is insisting on the "invocation" logic and not about the response handling logic. The API client can still invoke the API successfully and receive the response but the response will have a different datatype for some field.
>> Adding a new required field will break the API's invocation contract. When adding a new required field, the API contract breaks the RAML or API spec agreement that the API client/API consumer and API provider has between them. So this requires the API client invocation logic to also be updated.
質問 # 79
What should be ensured before sharing an API through a public Anypoint Exchange portal?
正解：C
解説：
Correct Answe r: The visibility level of the API instances of that API that need to be publicly accessible should be set to public visibility.
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Reference:
https://docs.mulesoft.com/exchange/to-share-api-asset-to-portal
質問 # 80
A company uses a hybrid Anypoint Platform deployment model that combines the EU control plane with customer-hosted Mule runtimes. After successfully testing a Mule API implementation in the Staging environment, the Mule API implementation is set with environment-specific properties and must be promoted to the Production environment. What is a way that MuleSoft recommends to configure the Mule API implementation and automate its promotion to the Production environment?
正解：A
解説：
Correct Answe r: Bundle properties files for each environment into the Mule API implementation's deployable archive, then promote the Mule API implementation to the Production environment using Anypoint CLI or the Anypoint Platform REST APIs
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>> Anypoint Exchange is for asset discovery and documentation. It has got no provision to modify the properties of Mule API implementations at all.
>> API Manager is for managing API instances, their contracts, policies and SLAs. It has also got no provision to modify the properties of API implementations.
>> API policies are to address Non-functional requirements of APIs and has again got no provision to modify the properties of API implementations.
So, the right way and recommended way to do this as part of development practice is to bundle properties files for each environment into the Mule API implementation and just point and refer to respective file per environment.
質問 # 81
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?
正解：B
解説：
Scenario Analysis:
The API implementation is experiencing high CPU usage (over 90%) during bursts of requests, which correlates with slow response times, as indicated by a 99th percentile response time greater than 120 seconds.
The API implementation is running on Anypoint Runtime Fabric with two non-clustered replicas and has a reserved vCPU of 1.0 and a vCPU limit of 2.0.
The high CPU usage during bursts suggests that the current resources may not be sufficient to handle peak loads.
Evaluating the Options:
Option A (Correct Answer): Increasing the vCPU resources for each replica would provide more processing power to handle high traffic volumes, potentially reducing the response time during spikes. Since the CPU usage is consistently high during bursts, this option directly addresses the resource bottleneck.
Option B: Modifying the API client to split requests may reduce individual request load but could be complex to implement on the client side and may not fully address the high CPU issue.
Option C: Increasing the number of replicas could help distribute the load; however, with a high CPU load on each replica, adding more replicas without increasing CPU resources may not fully resolve the problem.
Option D: Throttling the client would reduce the number of requests, but this may not be acceptable if the client needs to maintain a high request rate. It also does not directly address the CPU limitations of the API implementation.
Conclusion:
Option A is the best choice as it addresses the root cause of high CPU usage by increasing the vCPU allocation, allowing the API to handle more requests efficiently. This should be pursued first before considering other options.
Refer to MuleSoft's documentation on Runtime Fabric and vCPU resource allocation for more details on optimizing API performance in high-demand environments.
質問 # 82
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