Free Salesforce MuleSoft-Platform-Architect-I Practice Exam with Questions & Answers

Explanation

Correct Answer: 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.

References:

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-2 019

https://help.mulesoft.com/s/article/On-premise-Runtimes-Appear-Disconnected-in-Runtime-Manager-May-29th-2018

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Given that the application is designed to handle only one process at a time to maintain data consistency, here’s why horizontal scaling won’t increase the processing limit:

Single-Process Constraint:

The application limits to processing one transaction at a time due to its design for consistency, meaning horizontal scaling (adding more workers) does not increase processing speed beyond this limit.

Execution Time:

Since each request takes 200 ms, five requests per second is the maximum processing threshold. Increasing the number of workers does not bypass this single-process limitation.

Explanation of Correct Answer (A):

The scalability remains at five requests per second, as this constraint is intrinsic to the application’s design.

Explanation of Incorrect Options:

Option B suggests a change in execution time, which horizontal scaling does not affect.

Option C assumes doubling the throughput, which isn’t possible due to the single-threaded nature of the application.

Option D suggests horizontal scaling cannot apply, which is incorrect; however, scaling does not increase throughput in this context.

ReferencesFor more on understanding scaling and concurrency in Mule applications, see MuleSoft’s documentation on application performance and scaling limitations​.

Explanation

Correct Answer: 4.0.0

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As per semver.org semantic versioning specification:

Given a version number MAJOR.MINOR.PATCH, increment the:

- MAJOR version when you make incompatible API changes.

- MINOR version when you add functionality in a backwards compatible manner.

- PATCH version when you make backwards compatible bug fixes.

As per the scenario given in the question, the API implementation is completely changing its behavior. Although the format of the time is still being maintained as hh:mm:ss and there is no change in schema w.r.t format, the API will start functioning different after this change as the times are going to come completely different.

Example: Before the change, say, time is going as 09:00:00 representing the PST. Now on, after the change, the same time will go as 18:00:00 as Central European Summer Time is 9 hours ahead of Pacific Time.

>> This may lead to some uncertain behavior on API clients depending on how they are handling the times in the API response. All the API clients need to be informed that the API functionality is going to change and will return in CEST format. So, this considered as a MAJOR change and the version of API for this new change would be 4.0.0

Explanation

Correct Answer: They provide an additional layer of resilience on top of the underlying backend system, thereby insulating clients from extended failure of these systems.

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In API-led connectivity,

>> Experience APIs - allow for innovation at the user interface level by consuming the underlying assets without being aware of how data is being extracted from backend systems.

>> Process APIs - compose data from various sources and combine them with orchestration logic to create higher level value

>> System APIs - reduce the dependency on the underlying backend systems by helping unlock data from backend systems in a reusable and consumable way.

However, they NEVER promise that they provide an additional layer of resilience on top of the underlying backend system, thereby insulating clients from extended failure of these systems.

https://dzone.com/articles/api-led-connectivity-with-mule

Explanation

Correct Answer: Use a horizontal CloudHub autoscaling policy that triggers on CPU utilization greater than 70%

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The scenario in the question is very clearly stating that the usual traffic in the year is pretty well handled by the existing worker configuration with CPU running well below 70%. The problem occurs only "sometimes" occasionally when there is spike in the number of orders coming in.

So, based on above, We neither need to permanently increase the size of each worker nor need to permanently increase the number of workers. This is unnecessary as other than those "occasional" times the resources are idle and wasted.

We have two options left now. Either to use horizontal Cloudhub autoscaling policy to automatically increase the number of workers or to use vertical Cloudhub autoscaling policy to automatically increase the vCore size of each worker.

Here, we need to take two things into consideration:

1. CPU

2. Order Submission Rate to JMS Queue

>> From CPU perspective, both the options (horizontal and vertical scaling) solves the issue. Both helps to bring down the usage below 90%.

>> However, If we go with Vertical Scaling, then from Order Submission Rate perspective, as the application is still being load balanced with two workers only, there may not be much improvement in the incoming request processing rate and order submission rate to JMS queue. The throughput would be same as before. Only CPU utilization comes down.

>> But, if we go with Horizontal Scaling, it will spawn new workers and adds extra hand to increase the throughput as more workers are being load balanced now. This way we can address both CPU and Order Submission rate.

Hence, Horizontal CloudHub Autoscaling policy is the right and best answer.

Explanation

Correct Answer: Allow System APIs to return data that is NOT currently required by the identified Process or Experience APIs.

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>> All customizations for the end-user application should be handled in "Experience API" only. Not in Process API

>> We should use tiered approach but NOT always by creating exactly one API for each of the 3 layers. Experience APIs might be one but Process APIs and System APIs are often more than one. System APIs for sure will be more than one all the time as they are the smallest modular APIs built in front of end systems.

>> Process APIs can call System APIs as well as other Process APIs. There is no such anti-design pattern in API-Led connectivity saying Process APIs should not call other Process APIs.

So, the right answer in the given set of options that makes sense as per API-Led connectivity principles is to allow System APIs to return data that is NOT currently required by the identified Process or Experience APIs. This way, some future Process APIs can make use of that data from System APIs and we need NOT touch the System layer APIs again and again.

Explanation

Correct Answer: Set a timeout of 100ms; that leaves 400ms for other two downstream APIs to complete

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Key details to take from the given scenario:

>> Upstream API's designed SLA is 500ms (median). Lets ignore maximum SLA response times.

>> This API calls 3 downstream APIs sequentially and all these are of similar complexity.

>> The first downstream API is offering median SLA of 100ms, 80th percentile: 500ms; 95th percentile: 1000ms.

Based on the above details:

>> We can rule out the option which is suggesting to set 50ms timeout. Because, if the median SLA itself being offered is 100ms then most of the calls are going to timeout and time gets wasted in retried them and eventually gets exhausted with all retries. Even if some retries gets successful, the remaining time wont leave enough room for 2nd and 3rd downstream APIs to respond within time.

>> The option suggesting to NOT set a timeout as the invocation of this API is mandatory and so we must wait until it responds is silly. As not setting time out would go against the good implementation pattern and moreover if the first API is not responding within its offered median SLA 100ms then most probably it would either respond in 500ms (80th percentile) or 1000ms (95th percentile). In BOTH cases, getting a successful response from 1st downstream API does NO GOOD because already by this time the Upstream API SLA of 500 ms is breached. There is no time left to call 2nd and 3rd downstream APIs.

>> It is NOT true that no timeout is possible to meet the upstream APIs desired SLA.

As 1st downstream API is offering its median SLA of 100ms, it means MOST of the time we would get the responses within that time. So, setting a timeout of 100ms would be ideal for MOST calls as it leaves enough room of 400ms for remaining 2 downstream API calls.

Explanation

Correct Answer: They must be deployed to Anypoint Platform runtime planes that are managed by Anypoint Platform control planes, with both planes in the same Jurisdiction.

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>> As per legal regulations, all data processing to be performed within a certain jurisdiction. Meaning, the data in USA should reside within USA and should not go out. Same way, the data in EU should reside within EU and should not go out.

>> So, just encrypting the data in transit and at rest does not help to be compliant with the rules. We need to make sure that data does not go out too.

>> The data that we are talking here is not just about the messages that are published to Anypoint MQ. It includes the apps running, transaction states, application logs, events, metric info and any other metadata. So, just replacing Anypoint MQ with a locally hosted ActiveMQ does NOT help.

>> The data that we are talking here is not just about the key/value pairs that are stored in Object Store. It includes the messages published, apps running, transaction states, application logs, events, metric info and any other metadata. So, just avoiding using Object Store does NOT help.

>> The only option left and also the right option in the given choices is to deploy application on runtime and control planes that are both within the jurisdiction.

For an organization starting with API-led connectivity to integrate a siloed back-end system with a new CRM, the following approach aligns with best practices and MuleSoft’s Center for Enablement (C4E) guidance:

API-led Connectivity: This model organizes APIs into distinct layers (System, Process, and Experience) to improve reusability, modularity, and manageability.

System APIs are used to expose and unlock data from core systems (such as back-end applications or databases).

Process APIs orchestrate data across multiple systems and transform it as needed.

Experience APIs format the data specifically for consumption by applications or devices, such as the CRM in this case.

Step to Support Application Network:

Create a System API for the back-end system. This API should expose the necessary data to support integration with the CRM.

By creating a System API with a RESTful interface, data can be accessed in a standardized way, making it easier to integrate with other systems and supporting future scalability.

Why Option D is Correct:

Creating a System API aligns with the principle of API-led connectivity, ensuring that data is exposed in a reusable manner. This API can then be orchestrated by Process APIs as needed to meet CRM requirements and can easily be extended to other applications.

Explanation of Incorrect Options:

Option A (creating a business process model) does not directly enable connectivity or expose back-end data through APIs.

Option B is unnecessary at this stage; assessing CRM capabilities like OAuth 2.0 support is not directly related to creating the application network via System APIs.

Option C contradicts API-led best practices by suggesting a point-to-point integration, which API-led connectivity seeks to avoid due to its lack of flexibility and scalability.

ReferencesRefer to MuleSoft’s API-led Connectivity resources for a detailed framework on building scalable integration layers using System, Process, and Experience APIs​​.

Explanation

Correct Answer:

1. Decrease the IT delivery gap

2. Meet various business demands without increasing the IT capacity

3. Make consumption of assets at the rate of production.

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