참고: Fast2test에서 Google Drive로 공유하는 무료, 최신 Mule-Arch-201 시험 문제집이 있습니다: https://drive.google.com/open?id=1w2qeQC1M6bFJBxxZNCDVEePm3BozxUg9
Fast2test에는 전문적인 업계인사들이Salesforce Mule-Arch-201시험문제와 답에 대하여 연구하여, 시험준비중인 여러분들한테 유용하고 필요한 시험가이드를 제공합니다. 만약Fast2test의 제품을 구매하려면, 우리Fast2test에서는 아주 디테일 한 설명과 최신버전 최고품질의자료를 즉적중율이 높은 문제와 답을제공합니다.Salesforce Mule-Arch-201자료는 충분한 시험대비자료가 될 것입니다. 안심하시고 Fast2test가 제공하는 상품을 사용하시고, 100%통과 율을 확신합니다.
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Applying Integration Patterns | 11% | - Event-Driven Architecture (EDA) - Batch Processing - Integration Scenarios & Patterns |
| Topic 2: Architecting and Deploying API Implementations | 11% | - Disaster Recovery (DR) Strategies - Mule Runtime Architecture - High Availability (HA) Design |
| Topic 3: Monitoring and Analyzing Application Networks | 8% | - Alerting & Auditing - Anypoint Monitoring Tools - Analytics & Business Insights |
| Topic 4: Managing APIs | 12% | - API Versioning - API Lifecycle Management - API Security (Policies, SLAs) |
| Topic 5: Meeting API Quality Goals | 8% | - API Performance Optimization - Unit & Integration Testing - Resilience & Reliability Patterns |
| Topic 6: Deploying API Implementations to CloudHub | 11% | - CloudHub Architecture - CloudHub 2.0 & Runtime Fabric - Deployment Strategies |
| Topic 7: Establishing Organizational and Platform Foundations | 17% | - Platform Foundation Setup - Governance and Compliance Framework - IT Delivery & C4E Operating Model |
| Topic 8: Designing and Sharing APIs | 11% | - Anypoint Exchange for Sharing Assets - API Design Best Practices - API-Led Connectivity Approach |
| Topic 9: Understanding the MuleSoft Platform & Application Networks | 11% | - Application Network Fundamentals - C4E (Center for Enablement) Definition - MuleSoft Anypoint Platform Core Concepts |
>> Mule-Arch-201최신버전 시험공부자료 <<
Fast2test에서 발췌한 Salesforce인증 Mule-Arch-201덤프는 전문적인 IT인사들이 연구정리한 최신버전 Salesforce인증 Mule-Arch-201시험에 대비한 공부자료입니다. Salesforce인증 Mule-Arch-201 덤프에 있는 문제만 이해하고 공부하신다면Salesforce인증 Mule-Arch-201시험을 한방에 패스하여 자격증을 쉽게 취득할수 있을것입니다.
질문 # 37
A set of tests must be performed prior to deploying API implementations to a staging environment. Due to data security and access restrictions, untested APIs cannot be granted access to the backend systems, so instead mocked data must be used for these tests. The amount of available mocked data and its contents is sufficient to entirely test the API implementations with no active connections to the backend systems. What type of tests should be used to incorporate this mocked data?
정답：A
설명：
Correct Answe r: Unit tests (Whitebox)
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Reference:
As per general IT testing practice and MuleSoft recommended practice, Integration and Performance tests should be done on full end to end setup for right evaluation. Which means all end systems should be connected while doing the tests. So, these options are OUT and we are left with Unit Tests and Functional Tests.
As per attached reference documentation from MuleSoft:
Unit Tests - are limited to the code that can be realistically exercised without the need to run it inside Mule itself. So good candidates are Small pieces of modular code, Sub Flows, Custom transformers, Custom components, Custom expression evaluators etc.
Functional Tests - are those that most extensively exercise your application configuration. In these tests, you have the freedom and tools for simulating happy and unhappy paths. You also have the possibility to create stubs for target services and make them success or fail to easily simulate happy and unhappy paths respectively.
As the scenario in the question demands for API implementation to be tested before deployment to Staging and also clearly indicates that there is enough/ sufficient amount of mock data to test the various components of API implementations with no active connections to the backend systems, Unit Tests are the one to be used to incorporate this mocked data.
질문 # 38
Refer to the exhibits.
Which architectural constraint is compatible with the API-led connectivity architectural style?
정답：B
설명：
Understanding API-led Connectivity Layers:
In MuleSoft's API-led connectivity approach, APIs are categorized into three layers:
Experience Layer: This layer is responsible for providing data to the end-user applications and is often customized to meet the needs of different user interfaces.
Process Layer: This layer is used to orchestrate and combine data from multiple System APIs. It acts as a mediator and business logic layer without directly interacting with the backend systems.
System Layer: This layer provides direct access to the backend systems (e.g., databases, ERPs) and is usually focused on exposing atomic data operations.
Evaluating the Architectural Constraints:
Option A: Always using a strict tiered approach by creating exactly one API per layer is not necessarily an architectural constraint of API-led connectivity. While a layered approach is recommended, it is common to have multiple APIs in each layer as needed for different functionalities.
Option B (Correct Answer): In API-led connectivity, Process APIs are generally responsible for orchestrating calls to System APIs and should not call other Process APIs. This maintains a clear separation of concerns, ensuring that Process APIs aggregate data from System APIs only and provide it to Experience APIs.
Option C: System APIs are generally designed to provide only the necessary data to meet current business requirements. Allowing them to return extra data that is not needed by Process or Experience APIs is not a best practice, as it can lead to inefficiencies.
Option D: Customizations specific to end-user applications are typically handled at the Experience Layer rather than the Process Layer, as the Experience Layer is intended to tailor the data to fit the needs of each specific client or front-end application.
Conclusion:
Option B is the correct answer as it aligns with the API-led connectivity principles. In this architectural style, Process APIs should orchestrate System APIs but should avoid interacting with other Process APIs to keep a clear separation of responsibilities across the layers.
For additional details, refer to MuleSoft documentation on API-led connectivity best practices, particularly around the roles of each layer in API orchestration and data handling.
질문 # 39
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?
정답：B
설명：
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.
질문 # 40
Traffic is routed through an API proxy to an API implementation. The API proxy is managed by API Manager and the API implementation is deployed to a CloudHub VPC using Runtime Manager. API policies have been applied to this API. In this deployment scenario, at what point are the API policies enforced on incoming API client requests?
정답：C
설명：
Correct Answe r: At the API proxy
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>> API Policies can be enforced at two places in Mule platform.
>> One - As an Embedded Policy enforcement in the same Mule Runtime where API implementation is running.
>> Two - On an API Proxy sitting in front of the Mule Runtime where API implementation is running.
>> As the deployment scenario in the question has API Proxy involved, the policies will be enforced at the API Proxy.
질문 # 41
Refer to the exhibit.
what is true when using customer-hosted Mule runtimes with the MuleSoft-hosted Anypoint Platform control plane (hybrid deployment)?
정답：D
설명：
Correct Answe r: API implementations can run successfully in customer-hosted Mule runtimes, even when they are unable to communicate with the control plane.
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>> We CANNOT use Shared Load balancer to load balance APIs on customer hosted runtimes
>> For Hybrid deployment models, the on-premises are first connected to Runtime Manager using Runtime Manager agent. So, the connection is initiated first from On-premises to Runtime Manager. Then all control can be done from Runtime Manager.
>> Anypoint Runtime Manager CANNOT ensure automatic HA. Clusters/Server Groups etc should be configured before hand.
Only TRUE statement in the given choices is, API implementations can run successfully in customer-hosted Mule runtimes, even when they are unable to communicate with the control plane. There are several references below to justify this statement.
Reference:
https://docs.mulesoft.com/runtime-manager/deployment-strategies#hybrid-deployments
https://help.mulesoft.com/s/article/On-Premise-Runtimes-Disconnected-From-US-Control-Plane-June-18th-2018
https://help.mulesoft.com/s/article/Runtime-Manager-cannot-manage-On-Prem-Applications-and-Servers-from-US-Control-Plane-June-25th-2019
https://help.mulesoft.com/s/article/On-premise-Runtimes-Appear-Disconnected-in-Runtime-Manager-May-29th-2018
질문 # 42
......
현재 많은 IT인사들이 같은 생각하고 잇습니다. 그것은 바로Salesforce Mule-Arch-201인증시험자격증 취득으로 하여 IT업계의 아주 중요한 한걸음이라고 말입니다.그만큼Salesforce Mule-Arch-201인증시험의 인기는 말 그대로 하늘을 찌르고 잇습니다,
Mule-Arch-201퍼펙트 덤프 샘플문제 다운: https://kr.fast2test.com/Mule-Arch-201-premium-file.html
Fast2test Mule-Arch-201 최신 PDF 버전 시험 문제집을 무료로 Google Drive에서 다운로드하세요: https://drive.google.com/open?id=1w2qeQC1M6bFJBxxZNCDVEePm3BozxUg9