A company stores financial transaction data in two legacy systems. For each legacy system, a separate, dedicated System API (SAPI) exposes data for that legacy system. A Process API (PAPI) merges the data retrieved from ail of the System APIs into a common format. Several API clients call the PAPI through its public domain name.
The company now wants to expose a subset of financial data to a newly developed mobile application that uses a different Bounded Context Data Model. The company wants to follow MuleSoft's best practices for building out an effective application network.
Following MuleSoft's best practices, how can the company expose financial data needed by the mobile application in a way that minimizes the impact on the currently running API clients, API implementations, and support asset reuse?
Answer : A
To achieve the goal of exposing financial data to a new mobile application while following MuleSoft's best practices, the company should follow an API-led connectivity approach. This approach ensures minimal disruption to existing clients, maximizes reusability, and respects the separation of concerns across API layers.
of Solution:
Experience APIs for Client-Specific Requirements:
Create two new Experience APIs (EAPI-1 and EAPI-2) for the mobile application, tailored to meet the specific data and format requirements of the mobile application. These APIs encapsulate the client-specific needs and provide a custom interface without impacting other clients.
Process API Layer for Data Transformation:
By adding Mobile PAPI-2, we allow the mobile application to access the required subset of data, formatted according to the mobile application's requirements. This approach ensures that data transformation and aggregation are handled in the Process layer, maintaining consistency and reusability across different applications.
Reuse of System APIs:
Both the new Mobile PAPI-2 and existing PAPI-1 access data from System APIs (SAPI-1 and SAPI-2), which continue to expose data from each legacy system in a consistent, reusable manner. This avoids duplicating logic and ensures that data access remains centralized and manageable.
Why Option A is Correct:
Option A aligns with MuleSoft's best practices by isolating client-specific requirements in the Experience layer, utilizing Process APIs for data orchestration and transformation, and maintaining reusable System APIs for backend access.
This approach also ensures that the current API clients are not impacted, as new clients (e.g., the mobile app) interact with newly defined Experience APIs without modifying the existing API setup.
of Incorrect Options:
Option B: This option seems similar but lacks clarity on the separation of mobile-specific requirements and does not explicitly mention data transformation, which is essential in this scenario.
Option C: Creating a single mobile Experience API that exposes a subset of PAPI endpoints directly adds unnecessary complexity and may violate the separation of concerns, as transformation logic should not be in the Experience layer.
Option D: Deploying a new PAPI and using an API Proxy to redirect existing endpoints would add unnecessary complexity, disrupt the current API clients, and increase maintenance efforts.
Reference For additional guidance, refer to MuleSoft documentation on API-led connectivity best practices and best practices for structuring Experience, Process, and System APIs.
An API with multiple API implementations (Mule applications) is deployed to both CloudHub and customer-hosted Mule runtimes. All the deployments are managed by
the MuleSoft-hosted control plane. An alert needs to be triggered whenever an API implementation stops responding to API requests, even if no API clients have called the API implementation for some time.
What is the most effective out-of-the-box solution to create these alerts to monitor the API implementations?
Answer : A
In scenarios where multiple API implementations are deployed across different environments (CloudHub and customer-hosted runtimes), Anypoint Functional Monitoring is the most effective tool to monitor API availability and trigger alerts when an API implementation becomes unresponsive. Here's how it works:
Using Anypoint Functional Monitoring:
Functional Monitoring allows you to create monitors that periodically invoke specific endpoints on the API implementations, simulating a client request. This helps ensure that the API is responsive, even if no actual client requests are being made.
If an API implementation does not respond as expected, Functional Monitoring can generate alerts, notifying administrators of potential issues.
Why Option A is Correct:
By setting up Functional Monitoring to automatically invoke the API endpoints at regular intervals, you ensure continuous monitoring and alerting capabilities, which are especially useful for APIs that may experience periods of low or no traffic. This approach provides a proactive solution, allowing you to identify and address issues before actual users are impacted.
of Incorrect Options:
Option B suggests modifying client applications to trigger alerts, which is not a best practice as it shifts monitoring responsibility to clients, reducing control and consistency.
Option C involves handling exceptions within client applications, which does not address situations where no clients are making requests.
Option D proposes a Worker Not Responding alert in Runtime Manager, which is limited to worker-specific alerts and may not reliably monitor the API's actual responsiveness to requests.
Reference For further information, refer to MuleSoft documentation on Anypoint Functional Monitoring setup and usage for API availability monitoring.
A Mule application implements an API. The Mule application has an HTTP Listener whose connector configuration sets the HTTPS protocol and hard-codes the port
value. The Mule application is deployed to an Anypoint VPC and uses the CloudHub 1.0 Shared Load Balancer (SLB) for all incoming traffic.
Which port number must be assigned to the HTTP Listener's connector configuration so that the Mule application properly receives HTTPS API invocations routed through the
SLB?
Answer : B
When using CloudHub 1.0's Shared Load Balancer (SLB) for a Mule application configured with HTTPS in an Anypoint VPC, specific ports must be configured for the application to correctly route incoming traffic:
Port Requirement for SLB:
The CloudHub Shared Load Balancer for HTTPS traffic requires that applications listen on port 8092 for secure (HTTPS) communication. This port is reserved specifically for SSL traffic when using SLB with Anypoint VPCs.
Why Option B is Correct:
Setting the HTTP Listener's connector configuration to 8092 aligns with CloudHub requirements for HTTPS via the Shared Load Balancer.
of Incorrect Options:
Option A (8082) is used for non-HTTPS (HTTP) traffic.
Option C (80) and Option D (443) are standard web ports but are not applicable within CloudHub SLB's internal configuration for VPC routing.
Reference For more information on the Shared Load Balancer port configurations, refer to MuleSoft's documentation on CloudHub and VPC load balancer requirements.
An established communications company is beginning its API-led connectivity journey, The company has been using a successful Enterprise Data Model for many years. The company has identified a self-service account management app as the first effort for API-led, and it has identified the following APIs.
Experience layer: Mobile Account Management EAPI, Browser Account Management EAPI
Process layer: Customer Lookup PAPI, Service Lookup PAPI, Account Lookup PAPI
System layer: Customer SAPI, Account SAPI, Product SAPI, Service SAPI
According to MuleSoft's API-led connectivity approach, which API would not be served by the Enterprise Data Model?
Answer : C
In the API-led connectivity approach, APIs are categorized into Experience, Process, and System layers:
Enterprise Data Model Scope:
The Enterprise Data Model (EDM) generally supports System APIs and some Process APIs by defining standard data structures used across the organization. Experience APIs, however, are tailored to specific applications or interfaces and are less likely to be served directly by the EDM, as they may require customized data representations to meet the unique needs of each user interface.
Why Option C is Correct:
The Mobile Account Management EAPI serves mobile-specific needs and often requires data formatted differently from the standardized data models. Thus, it would be outside the direct scope of the EDM and might employ custom mappings to fit mobile application requirements.
of Incorrect Options:
Option A (Customer SAPI), Option B (Customer Lookup PAPI), and Option D (Service SAPI) would typically align with the EDM as they are closer to the core data and services the EDM supports.
Reference For additional guidance, review MuleSoft's best practices on API-led connectivity and data modeling.
A TemperatureSensors API instance is defined in API Manager in the PROD environment of the CAR_FACTORY business group. An AcmelemperatureSensors Mule
application implements this API instance and is deployed from Runtime Manager to the PROD environment of the CAR_FACTORY business group. A policy that requires a valid
client ID and client secret is applied in API Manager to the API instance.
Where can an API consumer obtain a valid client ID and client secret to call the AcmeTemperatureSensors Mule application?
Answer : D
When an API policy requiring a client ID and client secret is applied to an API instance in API Manager, API consumers must obtain these credentials through a registered client application. Here's how it works:
Anypoint Exchange and Client Applications:
To access secured APIs, API consumers need to create or register a client application in Anypoint Exchange. This process involves requesting access to the specific API, and once approved, the consumer can retrieve the client ID and client secret associated with the application.
Why Option D is Correct:
Option D accurately describes the process, as the client ID and client secret are generated and managed within Anypoint Exchange. API consumers can use these credentials to authenticate with the TemperatureSensors API.
of Incorrect Options:
Option A (secrets manager) is incorrect because client credentials for API access are not managed via secrets manager.
Option B (API Manager) is incorrect as API Manager manages policies but does not provide client-specific credentials.
Option C (Access Management) does not apply, as Access Management is primarily used for user roles and permissions, not API client credentials.
Reference For further details on managing client applications in Anypoint Exchange, consult MuleSoft documentation on client application registration and API security policies.
A developer from the Central IT team has created an initial version of the RAML definition in Design Center for an OAuth 2.0-protected System API and published it
to Exchange. Another developer from LoB IT discovered the System API in Exchange and would like to leverage it in the Process API.
What is the MuleSoft-recommended approach for Process API to invoke the System API?
Answer : C
In MuleSoft's ecosystem, when a Process API needs to consume a System API (published to Exchange and protected by OAuth 2.0), the recommended approach is to utilize the REST Connect Connector. Here's how it aligns with best practices:
Automated Connector Generation:
When a RAML or OAS specification is published in Exchange, MuleSoft automatically generates a REST Connect Connector for that API. This connector simplifies integration as it abstracts the complexity of making HTTP requests and handling OAuth authentication.
Streamlined Integration:
The Process API can import this generated connector from Exchange and configure OAuth credentials, streamlining secure access to the System API without manual HTTP setup.
Why Option C is Correct:
Using the REST Connect Connector directly leverages MuleSoft's automated tooling, minimizing manual configurations and ensuring a more maintainable integration.
of Incorrect Options:
Option A (importing an OAuth module) is unnecessary; OAuth is handled within the connector's configuration.
Option B (property YAML files with HTTP requests) involves manual setup, which is more error-prone and not recommended.
Option D (manually updating POM file) does not directly aid in invoking an API through Exchange.
Reference For more information on using REST Connect Connectors and OAuth integration in MuleSoft, refer to the MuleSoft documentation on API Management and Connectors.
An operations team is analyzing the effort needed to set up monitoring of their application network. They are looking at which API invocation metrics can be used to identify and predict trouble without having to write custom scripts or install additional analytics software or tools.
Which type of metrics can satisfy this goal of directly identifying and predicting failures?
Answer : A
To monitor an application network and predict issues without custom scripts, policy violation metrics are critical. They provide insights into potential problems by tracking instances where API usage does not conform to defined policies. Here's why this approach is suitable:
Predictive Monitoring:
Tracking API policy violations (such as rate limits or spike controls being hit) can indicate surges in traffic or misuse, which may lead to throttling or service degradation if not addressed.
By monitoring these violations, teams can proactively adjust limits or optimize API handling to prevent actual failures.
No Custom Scripting Needed:
Policy violation metrics are available within MuleSoft's Anypoint Monitoring, meaning there's no need to implement custom solutions or external tools to gather and interpret this data.
of Incorrect Options:
Option B (effectiveness based on reuse) does not directly predict failures.
Option C (past invocation counts) offers historical usage data but does not inherently identify issues.
Option D (ROI from API invocation) is a business metric and does not provide technical insights for failure prediction.
Reference For more details on leveraging policy violation metrics for proactive monitoring, refer to MuleSoft documentation on Anypoint Monitoring.