MULESOFT-PLATFORM-ARCHITECT-I TRUSTWORTHY SOURCE - LATEST MULESOFT-PLATFORM-ARCHITECT-I EXAM FEE

MuleSoft-Platform-Architect-I Trustworthy Source - Latest MuleSoft-Platform-Architect-I Exam Fee

MuleSoft-Platform-Architect-I Trustworthy Source - Latest MuleSoft-Platform-Architect-I Exam Fee

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Salesforce MuleSoft-Platform-Architect-I Exam Syllabus Topics:

TopicDetails
Topic 1
  • Monitoring and Analyzing Application Networks: It discusses Anypoint Platform components for data generation, collected metrics, and key alerts. This topic also includes specifying alerts to define Mule applications.
Topic 2
  • Designing APIs Using System, Process, and Experience Layers: Identifying suitable APIs for business processes, assigning them according to functional focus, and recommending data model approaches are its sub-topics.
Topic 3
  • Architecting and Deploying API Implementations: It covers important aspects like using auto-discovery, identifying VPC requirements, comparing hosting options and understanding testing methods. The topic also involves automated building, testing, and deploying in a DevOps setting.
Topic 4
  • Governing Web APIs on Anypoint Platform: This topic includes sub-topics related to managing API instances and environments, selecting API policies, enforcing API policies, securing APIs, and understanding OAuth 2.0 relationships.

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Salesforce Certified MuleSoft Platform Architect I Sample Questions (Q31-Q36):

NEW QUESTION # 31
An API implementation is deployed to CloudHub.
What conditions can be alerted on using the default Anypoint Platform functionality, where the alert conditions depend on the end-to-end request processing of the API implementation?

  • A. When the API is invoked by an unrecognized API client
  • B. When a particular API client invokes the API too often within a given time period
  • C. When the API receives a very high number of API invocations
  • D. When the response time of API invocations exceeds a threshold

Answer: D

Explanation:
Correct Answer : When the response time of API invocations exceeds a threshold
*****************************************
>> Alerts can be setup for all the given options using the default Anypoint Platform functionality
>> However, the question insists on an alert whose conditions depend on the end-to-end request processing of the API implementation.
>> Alert w.r.t "Response Times" is the only one which requires end-to-end request processing of API implementation in order to determine if the threshold is exceeded or not.


NEW QUESTION # 32
Which three tools automate the deployment of Mule applications?
Choose 3 answers

  • A. Anypoint Platform CLI
  • B. API Community Manager
  • C. Platform APIs
  • D. Mule Mayen plugin
  • E. Runtime Manager
  • F. Anypoint Studio

Answer: A,C,E

Explanation:
MuleSoft offers various tools to automate the deployment of Mule applications, which can streamline deployment and management processes. Here's how each tool supports automated deployment:
Runtime Manager:
Anypoint Runtime Manager is MuleSoft's web-based interface that allows users to deploy, manage, and monitor applications directly. It provides deployment automation through its user-friendly interface.
Anypoint Platform CLI:
The Anypoint CLI enables scripting of deployment and management tasks, making it possible to automate deployments via command-line scripts. This tool is ideal for CI/CD pipelines as it integrates with automated processes.
Platform APIs:
MuleSoft's Platform APIs allow programmatic access to deployment functions, enabling integration with external automation tools and CI/CD systems. These APIs facilitate deployment through RESTful calls, which can be automated for continuous delivery.
of Incorrect Options:
Option D (Anypoint Studio) is primarily for development and does not support deployment automation.
Option E (Maven Plugin) can be used for building and deploying Mule applications but isn't classified as a platform tool for deployment.
Option F (API Community Manager) is unrelated to deployment and instead focuses on managing API communities.
Reference
For detailed steps on automating deployments with these tools, refer to MuleSoft documentation on Runtime Manager, CLI, and Platform APIs.


NEW QUESTION # 33
Which scenario is suited for MUnit tests instead of integration tests?

  • A. When no mocking is permissible
  • B. When testing does not require knowledge of implementation details
  • C. For read-only interactions to any dependencies (such as other web APIs)
  • D. For tests that are implemented using SoapUI

Answer: C

Explanation:
MUnit is MuleSoft's testing framework for creating and running automated tests within Anypoint Studio. It is specifically designed for unit testing Mule applications and is best suited when testing doesn't require understanding the inner workings or implementation details of the components being tested.
Ideal Use Cases for MUnit:
MUnit is optimal when testing individual flows, functions, or components in isolation. This type of testing focuses on verifying the behavior of each unit without needing to understand the complete system.
Since unit tests do not require external integrations or dependencies to be live, mocking is commonly used in MUnit to simulate the behavior of external services and APIs.
Why Option B is Correct:
Option B aligns with the concept of unit testing, where the emphasis is on testing functionality rather than system integration. Integration tests, on the other hand, would require implementation knowledge and live endpoints, making them unsuitable for MUnit's scope.
of Incorrect Options:
Option A (read-only interactions) and Option C (no mocking) do not suit MUnit's typical testing environment as MUnit is designed with mocking capabilities to simulate dependencies.
Option D (SoapUI-based tests) suggests an external testing tool, while MUnit is specific to MuleSoft.
Reference
For more on MUnit best practices, refer to MuleSoft's MUnit documentation.


NEW QUESTION # 34
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 horizontal CloudHub autoscaling policy that triggers on CPU utilization greater than 70%
  • B. Permanently increase the size of each of the two (2) CloudHub workers by at least four times (4x) to one (1) vCore
  • C. Permanently increase the number of CloudHub workers by four times (4x) to eight (8) CloudHub workers
  • D. Use a vertical CloudHub autoscaling policy that triggers on CPU utilization greater than 70%

Answer: A

Explanation:
Correct Answer : Use a horizontal CloudHub autoscaling policy that triggers on CPU utilization greater than 70%
*****************************************
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 # 35
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 retry logic to the process API to handle intermittent failures by invoking the system 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. 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
  • D. 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

Answer: B

Explanation:
Correct Answer : 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.


NEW QUESTION # 36
......

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