Salesforce Certified MuleSoft Integration Architect I Valid Exam Guide & MuleSoft-Integration-Architect-I Free Pdf Vce & Salesforce Certified MuleSoft Integration Architect I Latest Practice Questions

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

Topic	Details
Topic 1	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 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	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 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	Initiating Integration Solutions on Anypoint Platform: Summarizing MuleSoft Catalyst and Catalyst Knowledge Hub, differentiating between functional and non-functional requirements, selecting features for designing and managing APIs, and choosing deployment options are its sub-topics.
Topic 6	Designing Integration Solutions to Meet Performance Requirements: This topic covers meeting performance and capacity goals, using streaming features, and processing large message sequences.
Topic 7	Designing Integration Solutions to Meet Security Requirements: This topic emphasizes securing access to the Anypoint Platform and APIs, using Anypoint Security, counteracting security vulnerabilities, and understanding audit logging capabilities.
Topic 8	Designing Automated Tests for Mule Applications: This topic covers unit test suites, and scenarios for integration and performance testing.
Topic 9	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.

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Salesforce Certified MuleSoft Integration Architect I Sample Questions (Q213-Q218):

NEW QUESTION # 213
An integration Mute application consumes and processes a list of rows from a CSV file. Each row must be read from the CSV file, validated, and the row data sent to a JMS queue, in the exact order as in the CSV file.
If any processing step for a row falls, then a log entry must be written for that row, but processing of other rows must not be affected.
What combination of Mute components is most idiomatic (used according to their intended purpose) when Implementing the above requirements?

A. For Each scope On Error Continue scope
B. VM connector first Successful scope On Error Propagate scope
C. Scatter-Gather component On Error Continue scope
D. Async scope On Error Propagate scope

Answer: A

Explanation:
* On Error Propagate halts execution and sends error to the client. In this scenario it's mentioned that
"processing of other rows must not be affected" so Option B and C are ruled out.
* Scatter gather is used to club multiple responses together before processing. In this scenario, we need sequential processing. So option A is out of choice.
* Correct answer is For Each scope & On Error Continue scope Below requirement can be fulfilled in the below way
1) Using For Each scope , which will send each row from csv file sequentially. each row needs to be sent sequentially as requirement is to send the message in exactly the same way as it is mentioned in the csv file
2) Also other part of requirement is if any processing step for a row fails then it should log an error but should not affect other record processing . This can be achieved using On error Continue scope on these set of activities. so that error will not halt the processing. Also logger needs to be added in error handling section so that it can be logged.
* Attaching diagram for reference. Here it's try scope, but similar would be the case with For Each loop.
Diagram Description automatically generated

NEW QUESTION # 214
An organization is designing an integration Mule application to process orders by submitting them to a back-end system for offline processing. Each order will be received by the Mule application through an HTTPS POST and must be acknowledged immediately. Once acknowledged, the order will be submitted to a back-end system. Orders that cannot be successfully submitted due to rejections from the back-end system will need to be processed manually (outside the back-end system).
The Mule application will be deployed to a customer-hosted runtime and is able to use an existing ActiveMQ broker if needed. The ActiveMQ broker is located inside the organization's firewall. The back-end system has a track record of unreliability due to both minor network connectivity issues and longer outages.
What idiomatic (used for their intended purposes) combination of Mule application components and ActiveMQ queues are required to ensure automatic submission of orders to the back-end system while supporting but minimizing manual order processing?

A. One or more On Error scopes to assist calling the back-end system
An Until Successful scope containing VM components for long retries
A persistent dead-letter VM queue configured in CloudHub
B. An Until Successful scope to call the back-end system
One or more ActiveMQ long-retry queues
One or more ActiveMQ dead-letter queues for manual processing
C. One or more On Error scopes to assist calling the back-end system
One or more ActiveMQ long-retry queues
A persistent dead-letter object store configured in the CloudHub Object Store service
D. A Batch Job scope to call the back-end system
An Until Successful scope containing Object Store components for long retries A dead-letter object store configured in the Mule application

Answer: B

NEW QUESTION # 215
A DevOps team has adequate observability of individual system behavior and performance, but it struggles to track the entire lifecycle of each request across different microservices.
Which additional observability approach should this team consider adopting?

A. Data mining
B. Metrics
C. Tracing
D. Analytics

Answer: C

Explanation:
To track the entire lifecycle of each request across different microservices, the DevOps team should consider adopting tracing. Distributed tracing allows teams to follow a request as it travels through the various microservices, providing visibility into how long each part of the request lifecycle takes and where any bottlenecks or failures occur. Tools like Zipkin, Jaeger, and AWS X-Ray are commonly used for this purpose.
Tracing complements metrics and logging by providing detailed insights into the interactions and dependencies between services.
References:
* Distributed Tracing in Microservices
* What is Distributed Tracing?

NEW QUESTION # 216
How does timeout attribute help inform design decisions while using JMS connector listening for incoming messages in an extended architecture (XA) transaction?

A. The timeout defines the time that is allowed to pass without the transaction ending explicitly and after the timeout expires, the transaction rolls back
B. The timeout specifies the time allowed to pass between receiving JMS messages on the same JMS connection and then after the timeout new JMS connection is established
C. After the timeout is exceeded, stale JMS consumer threads are destroyed and new threads are created
D. The time allowed to pass between committing the transaction and the completion of the mule flow and then after the timeout flow processing triggers an error

Answer: A

NEW QUESTION # 217
What metrics about API invocations are available for visualization in custom charts using Anypoint Analytics?

A. Request size, request HTTP verbs, response time
B. Request size, number of requests, response size, response time
C. Request size, number of requests, JDBC Select operation response time
D. Request size, number of requests, JDBC Select operation result set size

Answer: B

Explanation:
Correct answer is Request size, number of requests, response size, response time Analytics API Analytics can provide insight into how your APIs are being used and how they are performing. From API Manager, you can access the Analytics dashboard, create a custom dashboard, create and manage charts, and create reports.
From API Manager, you can get following types of analytics: - API viewing analytics - API events analytics - Charted metrics in API Manager It can be accessed using: http://anypoint.mulesoft.com/analytics API Analytics provides a summary in chart form of requests, top apps, and latency for a particular duration.
The custom dashboard in Anypoint Analytics contains a set of charts for a single API or for all APIs Each chart displays various API characteristics
- Requests size: Line chart representing size of requests in KBs
- Requests : Line chart representing number of requests over a period
- Response size : Line chart representing size of response in KBs
- Response time :Line chart representing response time in ms
* To check this, You can go to API Manager > Analytics > Custom Dashboard > Edit Dashboard > Create Chart > Metric Graphical user interface, chart Description automatically generated

NEW QUESTION # 218
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