MuleSoft-Platform-Architect-I Exam Dumps - Salesforce Certified MuleSoft Platform Architect 1 Exam (WI25)

In the API-led connectivity approach, each layer (System, Process, and Experience) has a distinct purpose:

System APIs: These APIs connect directly to backend systems to expose and unlock data in a standardized way.

Process APIs: These are responsible for orchestrating and processing data across different systems, combining information where needed.

Experience APIs: These are designed for specific user interfaces or applications, often transforming data formats to fit the needs of each consumer application.

Why Option A is Correct:

Process APIs are designed to combine data from multiple systems, which aligns with the function of aggregating policy information from both the life and general insurance systems. This aggregation logic would ideally reside in the Process layer, separating data retrieval from data orchestration.

Moving this functionality to the Process layer enables reusability and modularity, as other Experience APIs or services could also leverage the combined policy data if needed.

Explanation of Incorrect Options:

Option B (Presenting the SOAP-based response) would be managed by the Experience layer, as this layer adapts data formats for specific interfaces.

Option C (Authenticating and maintaining backend connections) would typically be handled within the System layer, where backend integration and security handling occurs.

Option D (Querying data) is the function of System APIs, which access the backend systems directly and expose the raw data without additional processing.

ReferencesFor further details on API-led architecture and the roles of each layer, refer to MuleSoft’s documentation on API-led connectivity and API layers​​.

CloudHub Object Store v2 is a managed key-value store provided by MuleSoft to support various use cases where temporary data storage is required. Here’s why Option D is correct:

Key Length Support: Object Store v2 allows storage of keys with a length of up to 300 characters, making it suitable for applications needing flexible and descriptive keys.

Limitations on Size:

Object Store v2 is not intended for large payload storage and has a recommended size limit below 10 MB for each value. Payloads exceeding 15 MB may cause performance issues and are better suited to a file storage system or database.

Option B is incorrect because storing payloads above 15 MB exceeds Object Store’s optimal usage specifications.

Key-Value Limits: Object Store v2 is designed for moderate, transient storage needs, and does not support unlimited storage. Thus, Option A is incorrect.

Backward Compatibility: Object Store v2 does not support Mule 4 applications running Object Store v1. Option C is incorrect as Object Store v1 and v2 are distinct.

ReferencesFor more on CloudHub Object Store v2, refer to MuleSoft documentation on Object Store limitations and configuration​.

Explanation

Correct Answer: Ask the Marketing Department to interact with a mocking implementation of the API using the automatically generated API Console.

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As per MuleSoft's IT Operating Model:

>> API consumers need NOT wait until the full API implementation is ready.

>> NO technical test-suites needs to be shared with end users to interact with APIs.

>> Anypoint Platform offers a mocking capability on all the published API specifications to Anypoint Exchange which also will be rich in documentation covering all details of API functionalities and working nature.

>> No needs of arranging days of workshops with end users for feedback.

API consumers can use Anypoint Exchange features on the platform and interact with the API using its mocking feature. The feedback can be shared quickly on the same to incorporate any changes.

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​.

In API-led design, an Experience API is enhanced to improve how data is delivered to end-user applications. One primary reason to enhance an Experience API is when new data standards, such as a Canonical Data Model, are adopted. Here’s why:

Canonical Data Model (CDM):

Adopting a CDM standardizes data representations across the organization, making APIs more consistent and easier to consume across various services and applications.

Updating the Experience API ensures that it delivers data in this standardized format, improving interoperability and reusability.

Explanation of Correct Answer (D):

A CDM impacts the structure and types of data the API provides, and this update would be directly relevant to an Experience API, as it is the primary point of interaction for applications.

Explanation of Incorrect Options:

Option A involves adapting to consumer-specific standards, which is against API-led design principles.

Option B involves changes in System APIs, which don’t directly mandate changes to the Experience API unless data formatting adjustments are required.

Option C (IP Allowlist) relates to security rather than API design and would not motivate a functional enhancement of the API.

ReferencesFor more details on the use of Canonical Data Models in API-led architecture, refer to MuleSoft’s guidelines on data standardization and Experience API best practices​.

Role of Process API in API-led Connectivity:

In MuleSoft’s API-led connectivity approach, a Process API is used to coordinate, aggregate, and orchestrate data from various System APIs. It is primarily responsible for implementing business logic that spans multiple backend systems or entities, transforming and combining data as needed to support business processes.

Process APIs are not directly exposed to end-user clients; rather, they work between System APIs and Experience APIs, providing business logic and orchestration capabilities.

Evaluating the Options:

Option A: Process APIs are not typically responsible for large data dumps or data synchronization. That function would be handled by a System API or a specialized batch process.

Option B: Managing direct, secure communication between back-end systems and end-user clients is typically the role of Experience APIs rather than Process APIs.

Option C (Correct Answer): Process APIs are designed to coordinate and orchestrate calls to multiple other APIs in the network, which supports the automation of business processes.

Option D: Securing and managing communication with end-user clients is typically the role of Experience APIs, not Process APIs.

Conclusion:

Option C is the correct answer, as the main role of a Process API is to coordinate and orchestrate interactions between other APIs, enabling business processes to function seamlessly across multiple systems.

Refer to MuleSoft's API-led connectivity documentation for further explanation of the roles and responsibilities of Process APIs in an application network.

Correct Answer: Persistent Object Store

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>> Redis distributed cache is performant but NOT out-of-the-box solution in Anypoint Platform

>> File-storage is neither performant nor out-of-the-box solution in Anypoint Platform

>> java.util.WeakHashMap needs a completely custom implementation of cache from scratch using Java code and is limited to the JVM where it is running. Which means the state in the cache is not worker aware when running on multiple workers. This type of cache is local to the worker. So, this is neither out-of-the-box nor worker-aware among multiple workers on cloudhub. https://www.baeldung.com/java-weakhashmap

>> Persistent Object Store is an out-of-the-box solution provided by Anypoint Platform which is performant as well as worker aware among multiple workers running on CloudHub. https://docs.mulesoft.com/object-store/

So, Persistent Object Store is the right answer.

Explanation

Correct Answer: SLA-based rate limiting

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>> Client Id enforement policy is a "Compliance" related NFR and does not help in maintaining the "Quality of Service (QoS)". It CANNOT and NOT meant for protecting the backend systems from scalability challenges.

>> IP Whitelisting and OAuth 2.0 token enforcement are "Security" related NFRs and again does not help in maintaining the "Quality of Service (QoS)". They CANNOT and are NOT meant for protecting the backend systems from scalability challenges.

Rate Limiting, Rate Limiting-SLA, Throttling, Spike Control are the policies that are "Quality of Service (QOS)" related NFRs and are meant to help in protecting the backend systems from getting overloaded.

https://dzone.com/articles/how-to-secure-apis