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Salesforce-MuleSoft-Developer-II Practice Questions

Question # 1
A Mule API receives a JSON payload and updates the target system with the payload. The developer uses JSON schemas to ensure the data is valid. How can the data be validation before posting to the target system?
A. Use a DataWeave 2.09 transform operation, and at the log of the DataWeave script, add: %dw 2.0 Import.json-moduls
B. Using the DataWeave if Else condition test the values of the payload against the examples included in the schema
C. Apply the JSON Schema policy in API Manager and reference the correct schema in the policy configuration
D. Add the JSON module dependency and add the validate-schema operation in the flow, configured to reference the schema


D. Add the JSON module dependency and add the validate-schema operation in the flow, configured to reference the schema

Explanation:

To validate the data before posting to the target system, the developer should add the JSON module dependency and add the validate-schema operation in the flow, configured to reference the schema. The JSON module provides a validate-schema operation that validates a JSON payload against a JSON schema and throws an error if the payload is invalid. References: https://docs.mulesoft.com/json-module/1.1/json-validateschema


Question # 2
XMLUnit
A. XMLUnit
B. Junit
C. MUnit Extensions Maven plugin
D. MUnit Maven plugin


C. MUnit Extensions Maven plugin

Explanation:

To unit test modules created with XML SDK, the developer needs to use the MUnit Extensions Maven plugin. This plugin allows testing XML SDK modules using MUnit by adding a dependency to the module under test and using a custom processor tag to invoke it. References: https://docs.mulesoft.com/mule-sdk/1.1/xml-sdk#testing



Question # 3
Which type of cache invalidation does the Cache scope support without having to write any additional code?
A. Write-through invalidation
B. White-behind invalidation
C. Time to live
D. Notification-based invalidation


C. Time to live

Explanation:

The Cache scope supports time to live (TTL) as a cache invalidation strategy without having to write any additional code. TTL specifies how long the cached response is valid before it expires and needs to be refreshed. The Cache scope also supports custom invalidation strategies using MEL or DataWeave expressions.

References:

https://docs.mulesoft.com/mule-runtime/4.3/cache-scope#cache_invalidation



Question # 4
Which statement is true when working with correlation IDS?
A. The HTTP Listener regenerates correlation IDs regardless of the HTTP request
B. The Anypoint MQ Connector automatically propagates correlation IDS
C. The HTTP Listener generates correlation IDS unless a correlation ID is received in the HTTP request
D. The VM Connector does not automatically propagate correction IDs


C. The HTTP Listener generates correlation IDS unless a correlation ID is received in the HTTP request

Explanation:

When working with correlation IDs, the HTTP Listener generates correlation IDs unless a correlation ID is received in the HTTP request. In that case, it propagates the received correlation ID throughout the flow execution. Correlation IDs are used to track events across different flows or applications.

References: https://docs.mulesoft.com/muleruntime/4.3/about-mule-message#message-attributes



Question # 5
A Mule application deployed to multiple Cloudhub 2.0 replicas needs to temporarily persist large files over 10MB between flow executions, and routinely needs to query whether the file data exists on separate executions. How can this be achieved?
A. Store the contents of the file on separate storage, and store the key and location of the file Object using Object Store v2
B. Use an in-memory Object Store
C. Store the key and full contents of the file in an Object Store
D. Store the key and full contents of the file, caching the filename and location between requests


A. Store the contents of the file on separate storage, and store the key and location of the file Object using Object Store v2

Explanation:

To temporarily persist large files over 10MB between flow executions, and routinely query whether the file data exists on separate executions, the developer should store the contents of the file on separate storage, and store the key and location of the file object using Object Store v2. This way, the developer can avoid storing large files in memory or exceeding the size limit of Object Store v2 (10MB per object). The developer can also use Object Store v2 operations to query, retrieve, or delete the file object by its key.

References:

https://docs.mulesoft.com/object-store/osv2-faq#can-i-store-files-inobject-store-v2



Question # 6
An order processing system is composed of multiple Mule application responsible for warehouse, sales and shipping. Each application communication using Anypoint MQ. Each message must be correlated against the original order ID for observability and tracing. How should a developer propagate the order ID as the correlation ID across each message?
A. Use the underlying HTTP request of Anypoint MQ to set the ‘X-CORRELATION_ID’ header to the order ID
B. Set a custom Anypoint MQ user property to propagate the order ID and set the correlation ID in the receiving applications.
C. Use the default correlation ID, Anypoint MQ will sutomatically propagate it.
D. Wrap all Anypoint MQ Publish operations within a With CorrelationID scope from the Tracing module, setting the correlation ID to the order ID



Explanation:

To propagate the order ID as the correlation ID across each message using Anypoint MQ, the developer should wrap all Anypoint MQ Publish operations within a With CorrelationID scope from the Tracing module, setting the correlation ID to the order ID. The With CorrelationID scope allows setting a custom correlation ID for any event that occurs within it. The Tracing module also enables distributed tracing across different Mule applications and services using Anypoint Monitoring.

References: https://docs.mulesoft.com/tracing-module/1.0/tracing-module-reference#with-correlation-id-scope https://docs.mulesoft.com/tracing-module/1.0/tracing-module-concepts



Question # 7
Which pattern should be used to invoke multiple HTTP APIs in parallel and roll back failed requests in sequence?
A. A database as a transactional outbox and an Until Successful router to retry any requests
B. A Parallel for Each scope with each HTTP request wrapped in a Try scope
C. Scatter-Gather as central Saga orchestrator for all API request with compensating actions for failing routes
D. VM queues as a reliability pattern with error handlers to roll back any requests


C. Scatter-Gather as central Saga orchestrator for all API request with compensating actions for failing routes

Explanation:

To invoke multiple HTTP APIs in parallel and roll back failed requests in sequence, the developer should use a Scatter-Gather router as a central Saga orchestrator for all API requests with compensating actions for failing routes. A Scatter-Gather router executes multiple routes concurrently and aggregates the results. A Saga orchestrator coordinates a series of actions across different services and handles failures by executing compensating actions. Therefore, using a Scatter-Gather router as a Saga orchestrator allows invoking multiple HTTP APIs in parallel and rolling back any failed requests in sequence.

References: https://docs.mulesoft.com/mule-runtime/4.3/scatter-gather-concept https://docs.mulesoft.com/mule-runtime/4.3/saga



Question # 8
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 one private location with all 10 APIs in a single schedule
C. From one public location with all 10 APIs in a single schedule
D. From 10 public locations with each API in its own schedule


C. From one public location with all 10 APIs in a single schedule

Explanation:

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.

References:

https://docs.mulesoft.com/functional-monitoring/fm-create-monitor#create-a-monitor



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