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MuleSoft-Integration-Architect-I Fragenkatalog & MuleSoft-Integration-Architect-I Zertifizierungsprüfung

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>> MuleSoft-Integration-Architect-I Fragenkatalog <<

Salesforce MuleSoft-Integration-Architect-I Zertifizierungsprüfung & MuleSoft-Integration-Architect-I Zertifizierung

Heutzutage, wo IT-Branche schnell entwickelt ist, müssen wir die IT-Fachleuten mit anderen Augen sehen. Sie haben uns viele unglaubliche Bequemlichkeiten nach ihrer spitzen Technik geboten und dem Staat sowie Unternehmen eine Menge Menschenkräfte sowie Ressourcen erspart. Sie beziehen sicher ein hohes Gehalt. Wollen Sie gleich wie sie werden? Dann müssen Sie zuerst die Salesforce MuleSoft-Integration-Architect-I Zertifizierungsprüfung bestehen.

Salesforce MuleSoft-Integration-Architect-I Prüfungsplan:

Thema
Einzelheiten

Thema 1

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.

Thema 2

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.

Thema 3

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.

Thema 4

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.

Thema 5

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.

Salesforce Certified MuleSoft Integration Architect I MuleSoft-Integration-Architect-I Prüfungsfragen mit Lösungen (Q224-Q229):

224. Frage
An XA transaction Is being configured that involves a JMS connector listening for Incoming JMS messages. What is the meaning of the timeout attribute of the XA transaction, and what happens after the timeout expires?

A. The time that Is allowed to pass between receiving JMS messages on the same JMS connection After the timeout, a new JMS connection Is established
B. The time that Is allowed to pass for state JMS consumer threads to be destroyed After the timeout, a new JMS consumer thread is created
C. The time that Is allowed to pass without the transaction being ended explicitly After the timeout, the transaction Is forcefully rolled-back
D. The time that is allowed to pass between committing the transaction and the completion of the Mule flow After the timeout, flow processing triggers an error

Antwort: C

Begründung:
* Setting a transaction timeout for the Bitronix transaction manager
* Set the transaction timeout either
- In wrapper.conf
- In CloudHub in the Properties tab of the Mule application deployment
* The default is 60 secs. It is defined as
mule.bitronix.transactiontimeout = 120
* This property defines the timeout for each transaction created for this manager.
If the transaction has not terminated before the timeout expires it will be automatically rolled back.
--------------------------------------------------------------------------------------------------------------------- Additional Info around Transaction Management:
Bitronix is available as the XA transaction manager for Mule applications
* To use Bitronix, declare it as a global configuration element in the Mule application
<bti:transaction-manager />
* Each Mule runtime can have only one instance of a Bitronix transaction manager, which is shared by all Mule applications
* For customer-hosted deployments, define the XA transaction manager in a Mule domain
- Then share this global element among all Mule applications in the Mule runtime

225. Frage
What is a key difference between synchronous and asynchronous logging from Mule applications?

A. Asynchronous logging produces more reliable audit trails with more accurate timestamps
B. Asynchronous logging can improve Mule event processing throughput while also reducing the processing time for each Mule event
C. Synchronous logging within an ongoing transaction writes log messages in the same thread that processes the current Mule event
D. Synchronous logging writes log messages in a single logging thread but does not block the Mule event being processed by the next event processor

Antwort: B

Begründung:
Types of logging:
A) Synchronous: The execution of thread that is processing messages is interrupted to wait for the log message to be fully handled before it can continue.
# The execution of the thread that is processing your message is interrupted to wait for the log message to be fully output before it can continue
# Performance degrades because of synchronous logging
# Used when the log is used as an audit trail or when logging ERROR/CRITICAL messages
# If the logger fails to write to disk, the exception would raise on the same thread that's currently processing the Mule event. If logging is critical for you, then you can rollback the transaction.
Chart, diagram Description automatically generated

Chart, diagram, box and whisker chart Description automatically generated

B) Asynchronous:
# The logging operation occurs in a separate thread, so the actual processing of your message won't be delayed to wait for the logging to complete
# Substantial improvement in throughput and latency of message processing
# Mule runtime engine (Mule) 4 uses Log4j 2 asynchronous logging by default
# The disadvantage of asynchronous logging is error handling.
# If the logger fails to write to disk, the thread doing the processing won't be aware of any issues writing to the disk, so you won't be able to rollback anything. Because the actual writing of the log gets differed, there's a chance that log messages might never make it to disk and get lost, if Mule were to crash before the buffers are flushed.
------------------------------------------------------------------------------------------------------------------ So Correct answer is: Asynchronous logging can improve Mule event processing throughput while also reducing the processing time for each Mule event

226. Frage
As a part of project requirement, client will send a stream of data to mule application. Payload size can vary between 10mb to 5GB. Mule application is required to transform the data and send across multiple sftp servers. Due to the cost cuttings in the organization, mule application can only be allocated one worker with size of 0.2 vCore.
As an integration architect , which streaming strategy you would suggest to handle this scenario?

A. File based repeatable storage
B. File based non-repeatable stream
C. In-memory non repeatable stream
D. In-memory repeatable stream

Antwort: A

Begründung:
As the question says that data needs to be sent across multiple sftp serves , we cannot use non-repeatable streams. The non-repeatable strategy disables repeatable streams, which enables you to read an input stream only once.
You cant use in memory storage because with 0.2 vcore you will get only 1 GB of heap memory. Hence application will error out for file more than 1 GB.
Hence the correct option is file base repeatable stream

227. Frage
A retail company is implementing a MuleSoft API to get inventory details from two vendors by Invoking each vendor's online applications. Due to network issues, the invocations to the vendor applications are timing out intermittently, but the requests are successful after re-invoking each vendor application.
What is the most performant way of implementing the API to invoke each vendor application and to retry invocations that generate timeout errors?

A. Use a Round-Robin scope to invoke each vendor application on a separate route.
Use a Try-Catch scope in each route to retry requests that raise timeout errors.
B. Use a For-Each scope to invoke the two vendor applications in series, one after the other.
Place the For-Each scope inside an Until-Successful scope to retry requests that raise timeout errors.
C. Use a Choice scope to Invoke each vendor application on a separate route.
Place the Choice scope inside an Until-Successful scope to retry requests that raise timeout errors.
D. Use a Scatter-Gather scope to invoke each vendor application on a separate route.
Use an Until-Successful scope in each route to retry requests that raise timeout errors.

Antwort: D

Begründung:
To efficiently handle the invocation of vendor applications and retry requests that generate timeout errors, the most performant way is:
* Scatter-Gather Scope: Use a Scatter-Gather scope to invoke each vendor application in parallel. This approach allows the API to send requests to both vendors simultaneously, reducing overall processing time.
* Until-Successful Scope: Inside each route of the Scatter-Gather scope, use an Until-Successful scope to handle retries. The Until-Successful scope will retry the request in case of timeout errors until the request succeeds or the maximum retry limit is reached.
This combination ensures that the API can handle intermittent network issues efficiently while minimizing redundant transactions and maximizing performance.
References
* MuleSoft Documentation on Scatter-Gather Scope
* MuleSoft Documentation on Until-Successful Scope and Error Handling

228. Frage
A leading eCommerce giant will use MuleSoft APIs on Runtime Fabric (RTF) to process customer orders.
Some customer-sensitive information, such as credit card information, is required in request payloads or is included in response payloads in some of the APIs. Other API requests and responses are not authorized to access some of this customer-sensitive information but have been implemented to validate and transform based on the structure and format of this customer-sensitive information (such as account IDs, phone numbers, and postal codes).
What approach configures an API gateway to hide sensitive data exchanged between API consumers and API implementations, but can convert tokenized fields back to their original value for other API requests or responses, without having to recode the API implementations?
Later, the project team requires all API specifications to be augmented with an additional non-functional requirement (NFR) to protect the backend services from a high rate of requests, according to defined service- level agreements (SLAs). The NFR's SLAs are based on a new tiered subscription level "Gold", "Silver", or
"Platinum" that must be tied to a new parameter that is being added to the Accounts object in their enterprise data model.
Following MuleSoft's recommended best practices, how should the project team now convey the necessary non-functional requirement to stakeholders?

A. Create a shared RAML fragment required to implement the NFR, list each API implementation endpoint in the RAML fragment, and publish the RAML fragment to Exchange
B. Update each API specification with comments about the NFR's SLAs and publish each modified API specification to Exchange
C. Update each API specification with a shared RAML fragment required to implement the NFR and publish the RAML fragment and each modified API specification to Exchange
D. Create and deploy API proxies in API Manager for the NFR, change the baseurl in each API specification to the corresponding API proxy implementation endpoint, and publish each modified API specification to Exchange

Antwort: C

Begründung:
To convey the necessary non-functional requirement (NFR) related to protecting backend services from a high rate of requests according to SLAs, the following steps should be taken:
* Create a Shared RAML Fragment: Develop a RAML fragment that defines the NFR, including the SLAs for different subscription levels ("Gold", "Silver", "Platinum"). This fragment should include the details on rate limiting and throttling based on the new parameter added to the Accounts object.
* Update API Specifications: Integrate the shared RAML fragment into each API specification. This ensures that the NFR is consistently applied across all relevant APIs.
* Publish to Exchange: Publish the updated API specifications and the shared RAML fragment to Anypoint Exchange. This makes the NFR visible and accessible to all stakeholders and developers, ensuring compliance and implementation consistency.
This approach ensures that the NFR is clearly communicated and applied uniformly across all API implementations.
References
* MuleSoft Documentation on RAML and API Specifications
* Best Practices for API Design and Documentation

229. Frage
......

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