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Valid MuleSoft-Integration-Architect-I Dumps & New MuleSoft-Integration-Architect-I Practice Materials

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

Topic
Details

Topic 1

Designing Integration Solutions to Meet Performance Requirements: This topic covers meeting performance and capacity goals, using streaming features, and processing large message sequences.

Topic 2

Designing Architecture Using Integration Paradigms: This topic focuses on creating high-level integration architectures using various paradigms. It includes API-led connectivity, web APIs and HTTP, event-driven APIs, and message brokers, and designing Mule application using messaging patterns and technologies.

Topic 3

Applying DevOps Practices and Operating Integration Solutions: Its sub-topics are related to designing CI
CD pipelines with MuleSoft plugins, automating interactions with Anypoint Platform, designing logging configurations, and identifying Anypoint Monitoring features.

Topic 4

Designing Integration Solutions to Meet Reliability Requirements: It includes selecting alternatives to traditional transactions, recognizing the purpose of various scopes and strategies, differentiating disaster recovery and high availability, and using local and XA transactions.

Topic 5

Designing and Developing Mule Applications: It includes selecting application properties, using fundamental features, designing with core routers, understanding the Salesforce Connector, and leveraging core connectors.

Topic 6

Designing Integration Solutions to Meet Persistence Requirements: It addresses the usage of VM queues and connectors, object stores and services, and stateful components configured with object stores.

Topic 7

Designing for the Runtime Plane Technology Architecture: It includes analyzing Mule runtime clusters, designing solutions for CloudHub, choosing Mule runtime domains, leveraging Mule 4 class loader isolation, and understanding the reactive event processing model.

Salesforce Certified MuleSoft Integration Architect I Sample Questions (Q37-Q42):

NEW QUESTION # 37
An organization has just developed a Mule application that implements a REST API. The mule application will be deployed to a cluster of customer hosted Mule runtimes.
What additional infrastructure component must the customer provide in order to distribute inbound API requests across the Mule runtimes of the cluster?

A. An HTTP Load Balancer
B. A database
C. A message broker
D. An Object Store

Answer: A

Explanation:
Correct answer is An HTTP Load Balancer.
Key thing to note here is that we are deploying application to customer hosted Mule runtime. This means we will need load balancer to route the requests to different instances of the cluster.
Rest all options are distractors and their requirement depends on project use case.

NEW QUESTION # 38
Refer to the exhibit.

A Mule application is being designed to be deployed to several CIoudHub workers. The Mule application's integration logic is to replicate changed Accounts from Satesforce to a backend system every 5 minutes.
A watermark will be used to only retrieve those Satesforce Accounts that have been modified since the last time the integration logic ran.
What is the most appropriate way to implement persistence for the watermark in order to support the required data replication integration logic?

A. Persistent VM Queue
B. Persistent Object Store
C. Persistent Cache Scope
D. Persistent Anypoint MQ Queue

Answer: B

Explanation:
* An object store is a facility for storing objects in or across Mule applications. Mule uses object stores to persist data for eventual retrieval.
* Mule provides two types of object stores:
1) In-memory store - stores objects in local Mule runtime memory. Objects are lost on shutdown of the Mule runtime.
2) Persistent store - Mule persists data when an object store is explicitly configured to be persistent.
In a standalone Mule runtime, Mule creates a default persistent store in the file system. If you do not specify an object store, the default persistent object store is used.
MuleSoft Reference: https://docs.mulesoft.com/mule-runtime/3.9/mule-object-stores

NEW QUESTION # 39
An organization uses Mule runtimes which are managed by Anypoint Platform - Private Cloud Edition. What MuleSoft component is responsible for feeding analytics data to non-MuleSoft analytics platforms?

A. Anypoint Runtime Manager
B. The Mule runtimes
C. Anypoint API Manager
D. Anypoint Exchange

Answer: A

Explanation:
Correct answer is Anypoint Runtime Manager
MuleSoft Anypoint Runtime Manager (ARM) provides connectivity to Mule Runtime engines deployed across your organization to provide centralized management, monitoring and analytics reporting. However, most enterprise customers find it necessary for these on-premises runtimes to integrate with their existing non MuleSoft analytics / monitoring systems such as Splunk and ELK to support a single pane of glass view across the infrastructure.
* You can configure the Runtime Manager agent to export data to external analytics tools.
Using either the Runtime Manager cloud console or Anypoint Platform Private Cloud Edition, you can:
--> Send Mule event notifications, including flow executions and exceptions, to Splunk or ELK.
--> Send API Analytics to Splunk or ELK. Sending data to third-party tools is not supported for applications deployed on CloudHub.
You can use the CloudHub custom log appender to integrate with your logging system. Reference:
https://docs.mulesoft.com/runtime-manager/ https://docs.mulesoft.com/release-notes/runtime-manager-agent
/runtime-manager-agent-release-notes
Diagram Description automatically generated

Additional Info:
It can be achieved in 3 steps:
1) register an agent to a runtime manager,
2) configure a gateway to enable API analytics to be sent to non MuleSoft analytics platform (Splunk for ex.)
- as highlighted in the following diagram and
3) setup dashboards.
Diagram Description automatically generated

NEW QUESTION # 40
An IT integration tram followed an API-led connectivity approach to implement an order-fulfillment business process. It created an order processing AP that coordinates stateful interactions with a variety of microservices that validate, create, and fulfill new product orders Which interaction composition pattern did the integration architect who designed this order processing AP| use?

A. Streaming
B. Multicasting
C. Orchestration
D. Aggregation

Answer: C

NEW QUESTION # 41
One of the backend systems involved by the API implementation enforces rate limits on the number of request a particle client can make.
Both the back-end system and API implementation are deployed to several non-production environments including the staging environment and to a particular production environment. Rate limiting of the back-end system applies to all non-production environments.
The production environment however does not have any rate limiting.
What is the cost-effective approach to conduct performance test of the API implementation in the non-production staging environment?

A. Use MUnit to simulate standard responses from the back-end system.
Then conduct performance test to identify other bottlenecks in the system
B. Conduct scaled-down performance tests in the staging environment against rate-limiting back-end system. Then upscale performance results to full production scale
C. Create a Mocking service that replicates the back-end system's
production performance characteristics
Then configure the API implementation to use the mocking
service and conduct the performance test
D. Including logic within the API implementation that bypasses in locations of the back-end system in the staging environment and invoke a Mocking service that replicates typical back-end system responses Then conduct performance test using this API implementation

Answer: C

NEW QUESTION # 42
......

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