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Salesforce Mule-Dev-301 Exam Syllabus Topics:

Section	Weight	Objectives
Secure Data at Rest and in Transit	20%	- Implement security policies - Manage keys and certificates - Manage secure environment properties - Expose and invoke APIs over HTTPS
Design and Implement API-led Connectivity	13%	- Implement API autodiscovery - Implement HTTP callbacks/webhooks - Implement server-side caching
Implement Performant and Reliable Mule Applications	27%	- Validate messages and payloads - Implement reliable messaging patterns - Handle HTTP API invocations and errors - Use ObjectStore for persistence - Optimize performance and throughput
Implement Maintainable and Modular Mule Applications and Maven Builds	25%	- Create reusable libraries - Execute tests with Maven - Implement automated deployment - Build custom policies and processors - Modularize applications and builds
Implement Monitorable Mule Applications	15%	- Monitor applications and runtimes - Implement logging and log management - Implement message correlation - Expose health check endpoints

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Salesforce Certified MuleSoft Developer II Mule-Dev-301 Prüfungsfragen mit Lösungen (Q53-Q58):

53. Frage
Which statement is true when working with correlation IDs?

A. The Anypoint MQ Connector automatically propagates correlation IDs
B. The VM Connector does not automatically propagate correlation IDs
C. The HTTP Listener regenerates correlation IDs regardless of the HTTP request
D. The HTTP Listener generates correlation IDs unless a correlation ID is received in the HTTP request

Antwort: D

Begründung:
When Mule creates a new event, it needs a correlation ID so log entries and processing activity associated with that event can be traced together. Mule first checks whether the event source already provides a correlation ID. For an HTTP Listener, headers such as X-CORRELATION-ID or MULE_CORRELATION_ID can supply that identifier.
MuleSoft explicitly states that if the source message contains a correlation ID, Mule uses it; if the source does not provide one, Mule generates a unique correlation ID. Therefore, option C accurately describes HTTP Listener behavior.
Option B is incorrect because Mule does not unconditionally replace an incoming correlation ID. Option D is also incorrect: MuleSoft specifically documents that Anypoint MQ does not automatically propagate Mule correlationId between publisher and subscriber. If an identifier must traverse Anypoint MQ, it should be included as a user property and then reapplied or used by the receiving application.
Correlation IDs are central to production observability because they let operations teams connect log records and processing stages to the same business execution.
Reference topics: Mule correlation IDs; HTTP Listener; X-CORRELATION-ID; distributed tracing; event correlation.
Official documentation: https://docs.mulesoft.com/mule-runtime/latest/correlation-id Official documentation: https://docs.mulesoft.com/mq/mq-faq

54. Frage
A company deploys 10 public APIs to CloudHub. Each API has its individual health endpoint defined. The platform operation team wants to configure API Functional Monitoring to monitor the health of the APIs periodically while minimizing operational overhead and cost.
How should API Functional Monitoring be configured?

A. From one public location with each API in its own schedule
B. From 10 public locations with each API in its own schedule
C. From one public location with all 10 APIs in a single schedule
D. From one private location with all 10 APIs in a single schedule

Antwort: C

Begründung:
To configure API Functional Monitoring to monitor the health of 10 public APIs periodically while minimizing operational overhead and cost, the developer should use one public location with all 10 APIs in a single schedule. A public location is a worker that runs in a CloudHub shared environment, which is cheaper and easier to maintain than a private location. A single schedule allows running all 10 APIs tests at the same time and frequency, which reduces complexity and resource consumption. Reference: https://docs.mulesoft.com/functional-monitoring/fm-create-monitor#create-a-monitor

55. Frage
Which type of cache invalidation does the Cache scope support without having to write any additional code?

A. Time to live
B. Write-through invalidation
C. Write-behind invalidation
D. Notification-based invalidation

Antwort: A

Begründung:
Mule's Cache scope is backed by a caching strategy, commonly using an Object Store. The caching strategy can directly configure expiration characteristics, including entry time to live (TTL). Once the TTL expires and the corresponding expiration processing occurs, cached entries are removed according to the configured Object Store policy without requiring application-specific invalidation logic.
MuleSoft's cache strategy documentation shows that the Object Store used by a caching strategy can define values such as entryTtl, expirationInterval, and maximum entries. A Cache component can then reference that strategy and automatically use those expiration settings.
Write-through and write-behind describe cache/database synchronization strategies in which cache updates are coordinated with writes to an underlying persistent data source. Mule's standard Cache scope does not automatically implement those patterns as an invalidation mode. Notification-based invalidation would similarly require external notification logic or explicit Mule processing to identify and invalidate affected keys.
TTL requires no such custom invalidation workflow. The developer establishes the lifetime when configuring the caching Object Store, after which Mule manages expiration automatically.
Therefore, Time to live is the cache invalidation mechanism directly available through standard Cache scope configuration.
Reference topics: Cache Scope; Object Store Caching Strategy; entryTtl; expirationInterval; automatic cache expiration.
Official documentation: https://docs.mulesoft.com/mule-runtime/latest/cache-scope-strategy

56. Frage
The Center for Enablement team published a common application as a reusable module to the central Nexus repository.
How can the common application be included in all API implementations?

A. Add a Maven dependency in the POM file with jar as <classifier>
B. Download the common application from Naxus and copy it to the src/main/resources folder in the API
C. Copy the common application's source XML file and out it in a new flow file in the src/main/mule folder
D. Add a Maven dependency in the PCM file with multiple-plugin as <classifier>

Antwort: A

Begründung:
To include a common application as a reusable module in all API implementations, the developer should add a Maven dependency in the POM file with jar as <classifier>. This way, the developer can reuse Mule code from another application by packaging it as a JAR file and adding it as a dependency in the POM file of the API implementation. The classifier element specifies that it is a JAR file. Reference: https://docs.mulesoft.com/mule-runtime/4.3/mmp-concept#add-a-maven-dependency-to-the-pom-file

57. Frage
Which pattern should be used to invoke multiple HTTP APIs in parallel and roll back any failed requests in sequence?

A. Scatter-Gather as central Saga orchestrator for all API requests with compensating actions for failing routes
B. A database as a transactional outbox and an Until Successful router to retry any requests
C. VM queues as a reliability pattern with error handlers to roll back any requests
D. A Parallel For Each scope with each HTTP request wrapped in a Try scope

Antwort: A

Begründung:
The requirement combines two architectural concerns: the downstream HTTP calls must execute concurrently, and completed remote operations must be reversible when another operation fails. Scatter- Gather is the appropriate Mule router for the concurrency requirement because it dispatches the event to multiple routes and executes those routes in parallel.
However, independent HTTP APIs cannot generally participate in a single ACID/XA transaction with Mule.
Once a remote API successfully commits an operation, Mule cannot simply perform a conventional transaction rollback across that service boundary. The appropriate distributed-transaction pattern is therefore a Saga, where successful steps have defined compensating operations that semantically undo their effects when a subsequent step fails.
Scatter-Gather also provides a composite error model. If one or more routes fail, Mule collects successful route results and route failures into a MULE:COMPOSITE_ROUTING error. A centralized orchestration
/error-handling flow can inspect those results and execute compensation logic in the required sequence.
Parallel For Each is designed for elements of a collection, not orchestration of several distinct API operations.
VM queues and transactional-outbox patterns address different reliability concerns and do not directly implement distributed compensation.
Reference topics: Scatter-Gather Router; Composite Routing Errors; Distributed Transactions; Compensating
/Saga Pattern.
Official documentation: https://docs.mulesoft.com/mule-runtime/4.3/scatter-gather-concept

58. Frage
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