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Mule-Arch-201질문과답 & Mule-Arch-201 100％시험패스공부자료

참고: Fast2test에서 Google Drive로 공유하는 무료, 최신 Mule-Arch-201 시험 문제집이 있습니다: https://drive.google.com/open?id=1jWAIMUHtRMnmZTr-kY0Bddibt15g094_

Salesforce Mule-Arch-201 덤프가 고객님의 기대를 가득 채워드릴수 있도록 정말로 노력하고 있는 Fast2test랍니다. Salesforce Mule-Arch-201 덤프는 pdf버전과 소프트웨어버전으로만 되어있었는데 최근에는 휴대폰에서가 사용가능한 온라인버전까지 개발하였습니다. 날따라 새로운 시스템을 많이 개발하여 고객님께 더욱 편하게 다가갈수 있는 Fast2test가 되겠습니다.

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>> Mule-Arch-201질문과 답 <<

높은 통과율 Mule-Arch-201질문과 답 공부문제

Salesforce Mule-Arch-201 덤프의 PDF 버전과 Software 버전의 내용은 동일합니다. PDF버전은 프린트 가능한 버전으로서 단독구매하셔도 됩니다. Software 버전은 테스트용으로 PDF 버전 공부를 마친후 시험전에 실력테스트 가능합니다. Software 버전은 PDF버전의 보조용이기에 단독 판매하지 않습니다. 소프트웨어버전까지 필요하신 분은 PDF버전을 구입하실때 공동구매하셔야 합니다.

최신 Salesforce MuleSoft Mule-Arch-201 무료샘플문제 (Q127-Q132):

질문 # 127
A system API is deployed to a primary environment as well as to a disaster recovery (DR) environment, with different DNS names in each environment. A process API is a client to the system API and is being rate limited by the system API, with different limits in each of the environments. The system API's DR environment provides only 20% of the rate limiting offered by the primary environment. What is the best API fault-tolerant invocation strategy to reduce overall errors in the process API, given these conditions and constraints?

A. Invoke the system API deployed to the primary environment; add timeout and retry logic to the process API to avoid intermittent failures; if it still fails, invoke a copy of the process API deployed to the DR environment
B. Invoke the system API deployed to the primary environment; add timeout and retry logic to the process API to avoid intermittent failures; if it still fails, invoke the system API deployed to the DR environment
C. In parallel, invoke the system API deployed to the primary environment and the system API deployed to the DR environment; add timeout and retry logic to the process API to avoid intermittent failures; add logic to the process API to combine the results
D. Invoke the system API deployed to the primary environment; add retry logic to the process API to handle intermittent failures by invoking the system API deployed to the DR environment

정답：B

설명：
Correct Answe r: Invoke the system API deployed to the primary environment; add timeout and retry logic to the process API to avoid intermittent failures; if it still fails, invoke the system API deployed to the DR environment
*****************************************
There is one important consideration to be noted in the question which is - System API in DR environment provides only 20% of the rate limiting offered by the primary environment. So, comparitively, very less calls will be allowed into the DR environment API opposed to its primary environment. With this in mind, lets analyse what is the right and best fault-tolerant invocation strategy.
1. Invoking both the system APIs in parallel is definitely NOT a feasible approach because of the 20% limitation we have on DR environment. Calling in parallel every time would easily and quickly exhaust the rate limits on DR environment and may not give chance to genuine intermittent error scenarios to let in during the time of need.
2. Another option given is suggesting to add timeout and retry logic to process API while invoking primary environment's system API. This is good so far. However, when all retries failed, the option is suggesting to invoke the copy of process API on DR environment which is not right or recommended. Only system API is the one to be considered for fallback and not the whole process API. Process APIs usually have lot of heavy orchestration calling many other APIs which we do not want to repeat again by calling DR's process API. So this option is NOT right.
3. One more option given is suggesting to add the retry (no timeout) logic to process API to directly retry on DR environment's system API instead of retrying the primary environment system API first. This is not at all a proper fallback. A proper fallback should occur only after all retries are performed and exhausted on Primary environment first. But here, the option is suggesting to directly retry fallback API on first failure itself without trying main API. So, this option is NOT right too.
This leaves us one option which is right and best fit.
- Invoke the system API deployed to the primary environment
- Add Timeout and Retry logic on it in process API
- If it fails even after all retries, then invoke the system API deployed to the DR environment.

질문 # 128
When could the API data model of a System API reasonably mimic the data model exposed by the corresponding backend system, with minimal improvements over the backend system's data model?

A. When a pragmatic approach with only limited isolation from the backend system is deemed appropriate
B. When the System API can be assigned to a bounded context with a corresponding data model
C. When the corresponding backend system is expected to be replaced in the near future
D. When there is an existing Enterprise Data Model widely used across the organization

정답：A

설명：
Correct Answe r: When a pragmatic approach with only limited isolation from the backend system is deemed appropriate.
*****************************************
General guidance w.r.t choosing Data Models:
>> If an Enterprise Data Model is in use then the API data model of System APIs should make use of data types from that Enterprise Data Model and the corresponding API implementation should translate between these data types from the Enterprise Data Model and the native data model of the backend system.
>> If no Enterprise Data Model is in use then each System API should be assigned to a Bounded Context, the API data model of System APIs should make use of data types from the corresponding Bounded Context Data Model and the corresponding API implementation should translate between these data types from the Bounded Context Data Model and the native data model of the backend system. In this scenario, the data types in the Bounded Context Data Model are defined purely in terms of their business characteristics and are typically not related to the native data model of the backend system. In other words, the translation effort may be significant.
>> If no Enterprise Data Model is in use, and the definition of a clean Bounded Context Data Model is considered too much effort, then the API data model of System APIs should make use of data types that approximately mirror those from the backend system, same semantics and naming as backend system, lightly sanitized, expose all fields needed for the given System API's functionality, but not significantly more and making good use of REST conventions.
The latter approach, i.e., exposing in System APIs an API data model that basically mirrors that of the backend system, does not provide satisfactory isolation from backend systems through the System API tier on its own. In particular, it will typically not be possible to "swap out" a backend system without significantly changing all System APIs in front of that backend system and therefore the API implementations of all Process APIs that depend on those System APIs! This is so because it is not desirable to prolong the life of a previous backend system's data model in the form of the API data model of System APIs that now front a new backend system. The API data models of System APIs following this approach must therefore change when the backend system is replaced.
On the other hand:
>> It is a very pragmatic approach that adds comparatively little overhead over accessing the backend system directly
>> Isolates API clients from intricacies of the backend system outside the data model (protocol, authentication, connection pooling, network address, ...)
>> Allows the usual API policies to be applied to System APIs
>> Makes the API data model for interacting with the backend system explicit and visible, by exposing it in the RAML definitions of the System APIs
>> Further isolation from the backend system data model does occur in the API implementations of the Process API tier

질문 # 129
A company is building an application network using MuleSoft's recommendations for various API layers.
What is the main (default) role of a process API in an application network?

A. To manage and process the secure direct communication between a back-end system and an end-user client of mobile device in the application network
B. To automate parts of business processes by coordinating and orchestrating the invocation of other APIs in the application network
C. To secure, Manage, and process communication with specific types of end-user client applications or devices in the application network
D. To secure and optimize the data synchronization processing of large data dumps between back-end systems

정답：B

설명：
Role of Process API in API-led Connectivity:
In MuleSoft's API-led connectivity approach, a Process API is used to coordinate, aggregate, and orchestrate data from various System APIs. It is primarily responsible for implementing business logic that spans multiple backend systems or entities, transforming and combining data as needed to support business processes.
Process APIs are not directly exposed to end-user clients; rather, they work between System APIs and Experience APIs, providing business logic and orchestration capabilities.
Evaluating the Options:
Option A: Process APIs are not typically responsible for large data dumps or data synchronization. That function would be handled by a System API or a specialized batch process.
Option B: Managing direct, secure communication between back-end systems and end-user clients is typically the role of Experience APIs rather than Process APIs.
Option C (Correct Answer): Process APIs are designed to coordinate and orchestrate calls to multiple other APIs in the network, which supports the automation of business processes.
Option D: Securing and managing communication with end-user clients is typically the role of Experience APIs, not Process APIs.
Conclusion:
Option C is the correct answer, as the main role of a Process API is to coordinate and orchestrate interactions between other APIs, enabling business processes to function seamlessly across multiple systems.
Refer to MuleSoft's API-led connectivity documentation for further explanation of the roles and responsibilities of Process APIs in an application network.

질문 # 130
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?

A. Rate limiting - SLA based
B. Rate limiting
C. HTTP caching
D. Spike control

정답：D

설명：
Correct Answe r: Spike control
*****************************************
>> 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.

질문 # 131
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?

A. Modify the API client to split the problematic request into smaller, less-demanding requests
B. Increase the vCPU resources of the API implementation
C. Increase the number of replicas of the API implementation
D. Throttle the APT client to reduce the number of requests per minute

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 # 132
......

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