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Latest Mule-Arch-201 Exam Materials: Salesforce Certified MuleSoft Platform Architect give you the most helpful Training DumpsUp to now, we have business connection with tens of thousands of exam candidates who adore the quality of our Mule-Arch-201 exam questions. Besides, we try to keep our services brief, specific and courteous with reasonable prices of Mule-Arch-201 Study Guide. All your questions will be treated and answered fully and promptly. So as long as you contact us to ask for the questions on the Mule-Arch-201 learning guide, you will get the guidance immediately. Salesforce Certified MuleSoft Platform Architect Sample Questions (Q128-Q133):NEW QUESTION # 128
A TemperatureSensors API instance is defined in API Manager in the PROD environment of the CAR_FACTORY business group. An AcmelemperatureSensors Mule application implements this API instance and is deployed from Runtime Manager to the PROD environment of the CAR_FACTORY business group. A policy that requires a valid client ID and client secret is applied in API Manager to the API instance.
Where can an API consumer obtain a valid client ID and client secret to call the AcmeTemperatureSensors Mule application?
A. In API Manager, from the PROD environment of the CAR_FACTORY business group
B. In access management, from the PROD environment of the CAR_FACTORY business group
C. In secrets manager, request access to the Shared Secret static username/password
D. In Anypoint Exchange, from an API client application that has been approved for the TemperatureSensors API instance
Answer: D
Explanation:
When an API policy requiring a client ID and client secret is applied to an API instance in API Manager, API consumers must obtain these credentials through a registered client application. Here's how it works:
Anypoint Exchange and Client Applications:
To access secured APIs, API consumers need to create or register a client application in Anypoint Exchange. This process involves requesting access to the specific API, and once approved, the consumer can retrieve the client ID and client secret associated with the application.
Why Option D is Correct:
Option D accurately describes the process, as the client ID and client secret are generated and managed within Anypoint Exchange. API consumers can use these credentials to authenticate with the TemperatureSensors API.
of Incorrect Options:
Option A (secrets manager) is incorrect because client credentials for API access are not managed via secrets manager.
Option B (API Manager) is incorrect as API Manager manages policies but does not provide client-specific credentials.
Option C (Access Management) does not apply, as Access Management is primarily used for user roles and permissions, not API client credentials.
Reference
For further details on managing client applications in Anypoint Exchange, consult MuleSoft documentation on client application registration and API security policies.
NEW QUESTION # 129
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 there is an existing Enterprise Data Model widely used across the organization
B. When the System API can be assigned to a bounded context with a corresponding data model
C. When a pragmatic approach with only limited isolation from the backend system is deemed appropriate
D. When the corresponding backend system is expected to be replaced in the near future
Answer: C
Explanation:
Correct Answe r: When a pragmatic approach with only limited isolation from the backend system is deemed appropriate.
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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
NEW QUESTION # 130
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?
A. At a MuleSoft-hosted load balancer
B. At the API implementation
C. At both the API proxy and the API implementation
D. At the API proxy
Answer: D
Explanation:
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.
NEW QUESTION # 131
What is a typical result of using a fine-grained rather than a coarse-grained API deployment model to implement a given business process?
A. A better response time for the end user as a result of the APIs being smaller in scope and complexity
B. A decrease in the number of connections within the application network supporting the business process
C. A higher number of discoverable API-related assets in the application network
D. An overall tower usage of resources because each fine-grained API consumes less resources
Answer: C
Explanation:
Correct Answe r: A higher number of discoverable API-related assets in the application network.
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>> We do NOT get faster response times in fine-grained approach when compared to coarse-grained approach.
>> In fact, we get faster response times from a network having coarse-grained APIs compared to a network having fine-grained APIs model. The reasons are below.
Fine-grained approach:
1. will have more APIs compared to coarse-grained
2. So, more orchestration needs to be done to achieve a functionality in business process.
3. Which means, lots of API calls to be made. So, more connections will needs to be established. So, obviously more hops, more network i/o, more number of integration points compared to coarse-grained approach where fewer APIs with bulk functionality embedded in them.
4. That is why, because of all these extra hops and added latencies, fine-grained approach will have bit more response times compared to coarse-grained.
5. Not only added latencies and connections, there will be more resources used up in fine-grained approach due to more number of APIs.
That's why, fine-grained APIs are good in a way to expose more number of resuable assets in your network and make them discoverable. However, needs more maintenance, taking care of integration points, connections, resources with a little compromise w.r.t network hops and response times.
NEW QUESTION # 132
A retail company is using an Order API to accept new orders. The Order API uses a JMS queue to submit orders to a backend order management service. The normal load for orders is being handled using two (2) CloudHub workers, each configured with 0.2 vCore. The CPU load of each CloudHub worker normally runs well below 70%. However, several times during the year the Order API gets four times (4x) the average number of orders. This causes the CloudHub worker CPU load to exceed 90% and the order submission time to exceed 30 seconds. The cause, however, is NOT the backend order management service, which still responds fast enough to meet the response SLA for the Order API. What is the MOST resource-efficient way to configure the Mule application's CloudHub deployment to help the company cope with this performance challenge?
A. Use a vertical CloudHub autoscaling policy that triggers on CPU utilization greater than 70%
B. Permanently increase the number of CloudHub workers by four times (4x) to eight (8) CloudHub workers
C. Permanently increase the size of each of the two (2) CloudHub workers by at least four times (4x) to one (1) vCore
D. Use a horizontal CloudHub autoscaling policy that triggers on CPU utilization greater than 70%
Answer: D
Explanation:
Correct Answe r: 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.
NEW QUESTION # 133
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