Salesforce Updated MuleSoft-Platform-Architect-I Exam Questions and Answers by georgie

MUnit is MuleSoft’s testing framework for creating and running automated tests within Anypoint Studio. It is specifically designed for unit testing Mule applications and is best suited when testing doesn’t require understanding the inner workings or implementation details of the components being tested.

Ideal Use Cases for MUnit:

MUnit is optimal when testing individual flows, functions, or components in isolation. This type of testing focuses on verifying the behavior of each unit without needing to understand the complete system.

Since unit tests do not require external integrations or dependencies to be live, mocking is commonly used in MUnit to simulate the behavior of external services and APIs.

Why Option B is Correct:

Option B aligns with the concept of unit testing, where the emphasis is on testing functionality rather than system integration. Integration tests, on the other hand, would require implementation knowledge and live endpoints, making them unsuitable for MUnit’s scope.

Explanation of Incorrect Options:

Option A (read-only interactions) and Option C (no mocking) do not suit MUnit’s typical testing environment as MUnit is designed with mocking capabilities to simulate dependencies.

Option D (SoapUI-based tests) suggests an external testing tool, while MUnit is specific to MuleSoft.

ReferencesFor more on MUnit best practices, refer to MuleSoft’s MUnit documentation​.

To create a unified schema in MuleSoft's DataGraph, APIs must be exposed in a way that allows DataGraph to pull and consolidate data from these APIs into a single schema accessible to consumers. DataGraph provides a federated approach, combining multiple APIs to form a single, unified API endpoint.

In this setup:

APIs 1, 2, 3, and 4 are suitable candidates for DataGraph because they are hosted within the Customer VPC on CloudHub and are accessible either through a Shared Load Balancer (LB) or a Dedicated Load Balancer (DLB). Both of these load balancers provide public access, which is a necessary condition for DataGraph as it must access the APIs to aggregate data.

APIs 5 and 6 are hosted on Customer Hosted Server 2, which is explicitly marked as "Not public". Since DataGraph requires API access through a publicly reachable endpoint to aggregate them into a unified schema, APIs 5 and 6 cannot be used with DataGraph in this configuration.

APIs 3 and 4 on Customer Hosted Server 1 appear accessible through a Shared LB, implying public accessibility that meets DataGraph’s requirements.

By combining APIs 1, 2, 3, and 4 within DataGraph, you can create a unified schema that enables clients to query data seamlessly from all these APIs as if it were from a single source.

This setup allows for efficient data retrieval and can simplify API consumption by reducing the need to call multiple APIs individually, thus optimizing performance and developer experience.

ReferencesFor more detailed information on setting up and managing unified schemas in DataGraph, refer to the DataWeave documentation and MuleSoft DataGraph resources which provide in-depth guidelines on schema aggregation and API federation​​.

In MuleSoft CloudHub, autoscaling is essential to managing application load efficiently. CloudHub supports horizontal scaling based on CPU usage, which is well-suited to applications experiencing variable demand and needing responsive resource allocation.

Autoscaling on CloudHub:

Horizontal scaling increases the number of workers in response to CPU usage thresholds, allowing the application to handle higher loads dynamically. This approach improves performance without downtime or manual intervention.

Why Option C is Correct:

Setting up autoscaling based on CPU usage aligns with MuleSoft’s best practices for scalable and responsive applications on CloudHub, particularly in an environment with fluctuating load patterns.

Option C correctly leverages CloudHub’s autoscaling features based on resource metrics, which are part of CloudHub’s managed scaling solutions.

Explanation of Incorrect Options:

Option A (based on HTTP request thresholds) and Option B (separate policies for CPU and memory) do not represent CloudHub’s recommended scaling practices.

Option D suggests vertical scaling based on response time, which is not how CloudHub handles autoscaling.

ReferencesFor more on CloudHub’s autoscaling configuration, refer to MuleSoft documentation on CloudHub autoscaling policies​.