Pass the MuleSoft MuleSoft Certified Architect MCIA-Level-1 Questions and answers with CertsForce

Explanation

Correct answer is Untill Successful scope can be implemented while calling backend API's The Until Successful scope repeatedly triggers the scope's components (including flow references) until they all succeed or until a maximum number of retries is exceeded The scope provides option to control the max number of retries and the interval between retries The scope can execute any sequence of processors that may fail for whatever reason and may succeed upon retry

If the size of the stream exceeds the maximum, a STREAM_MAXIMUM_SIZE_EXCEEDED error is raised.

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.

In a typical REST request, the order of interaction is:

API Client: The client initiates the request to access data or functionality exposed by the API.

API Interface: This represents the contract or the definition of the API, often specified in OpenAPI or RAML. It defines the endpoints, request/response formats, and other API details.

API Implementation: This is the actual backend logic that processes the request and returns the response. It interacts with databases, other services, or performs business logic to fulfill the request.

References:

Understanding REST APIs

API Design and Implementation

Explanation

Correct answer is EACH item VM message is processed AT LEAST ONCE by ONE ARBITRARY CloudHub worker. Each of the four CloudHub workers can be expected to process some item VM messages In Cloudhub, each persistent VM queue is listened on by every CloudHub worker - But each message is read and processed at least once by only one CloudHub worker and the duplicate processing is possible - If the CloudHub worker fails , the message can be read by another worker to prevent loss of messages and this can lead to duplicate processing - By default , every CloudHub worker's VM Listener receives different messages from VM Queue Referenece: https://dzone.com/articles/deploying-mulesoft-application-on-1-worker-vs-mult

Explanation

Context of the question is about managing and governing mule applications deployed on Anypoint platform.

Anypoint API Manager (API Manager) is a component of Anypoint Platform that enables you to manage, govern, and secure APIs. It leverages the runtime capabilities of API Gateway and Anypoint Service Mesh, both of which enforce policies, collect and track analytics data, manage proxies, provide encryption and authentication, and manage applications.

Mule Ref Doc : https://docs.mulesoft.com/api-manager/2.x/getting-started-proxy

Explanation

Connected Apps

The Connected Apps feature provides a framework that enables an external application to integrate with Anypoint Platform using APIs through OAuth 2.0 and OpenID Connect. Connected apps help users delegate their access without sharing sensitive credentials or giving full control of their accounts to third parties. Actions taken by connected apps are audited, and users can also revoke access at any time. Note that some products do not currently include client IDs in this release of the Connected Apps feature. The Connected Apps feature enables you to use secure authentication protocols and control an app’s access to user data. Additionally, end users can authorize the app to access their Anypoint Platform data.

Mule Ref Doc : https://docs.mulesoft.com/access-management/connected-apps-overview

In the given scenario, a rate limiting policy has been applied to the SOAP API and a global error handler is configured to handle HTTP:RETRY_EXHAUSTED errors with a 429 status code. The rate limiting policy will trigger when the client exceeds the allowed quota of API calls. Since the HTTP:RETRY_EXHAUSTED error specifically catches quota exhaustion errors and the error handler is configured to return a 429 status code, the expected HTTP status returned to the client when the quota is exceeded will be 429. This error code indicates that the user has sent too many requests in a given amount of time ("rate limiting").

References:

MuleSoft Documentation on Error Handling

HTTP Status Codes

When a Mule application using VM (Virtual Machine) queues is deployed to a customer-hosted cluster or multiple CloudHub workers, the messages are consumed by the Mule engine in a non-deterministic way. Here’s an in-depth explanation:

VM Queues Overview:

VM queues in Mule applications are used for intra-application communication, allowing different flows within the same application to exchange messages.

Deployment in Clusters or Multiple Workers:

Customer-Hosted Cluster: In a customer-hosted cluster, multiple Mule runtime instances work together to process messages. Each instance can pick up messages from the VM queue.

CloudHub Workers: When deployed on CloudHub, multiple worker instances can run the same Mule application, and each worker can access the VM queue.

Non-Deterministic Message Consumption:

Load Distribution: Messages are distributed among the available nodes or workers based on their availability. This means any node or worker that is ready to process a message can pick it up from the queue.

No Guaranteed Order: Because any available node or worker can consume messages, the order in which messages are processed is not guaranteed, making the consumption non-deterministic.

Parallel Processing: This approach allows for parallel processing of messages, which improves the scalability and throughput of the application.

Advantages:

Scalability: By allowing multiple nodes or workers to process messages, the system can handle increased load more effectively.

Fault Tolerance: If one node or worker fails, other nodes/workers can continue processing messages from the VM queue, providing higher availability.

Considerations:

Idempotency: Ensure that the processing logic is idempotent, meaning that processing the same message more than once does not produce different outcomes. This is crucial in a non-deterministic consumption environment to avoid issues with data consistency.

Transaction Management: Proper transaction management should be in place to handle scenarios where a message might need to be reprocessed due to errors.

References:

MuleSoft Documentation on VM Connector

MuleSoft Documentation on High Availability Clustering

MuleSoft Documentation on Deploying to CloudHub