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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Applying Integration Patterns | 11% | - Integration Scenarios & Patterns - Event-Driven Architecture (EDA) - Batch Processing |
| Topic 2: Managing APIs | 12% | - API Lifecycle Management - API Security (Policies, SLAs) - API Versioning |
| Topic 3: Establishing Organizational and Platform Foundations | 17% | - Governance and Compliance Framework - IT Delivery & C4E Operating Model - Platform Foundation Setup |
| Topic 4: Monitoring and Analyzing Application Networks | 8% | - Analytics & Business Insights - Alerting & Auditing - Anypoint Monitoring Tools |
| Topic 5: Deploying API Implementations to CloudHub | 11% | - Deployment Strategies - CloudHub Architecture - CloudHub 2.0 & Runtime Fabric |
| Topic 6: Understanding the MuleSoft Platform & Application Networks | 11% | - Application Network Fundamentals - MuleSoft Anypoint Platform Core Concepts - C4E (Center for Enablement) Definition |
| Topic 7: Designing and Sharing APIs | 11% | - API Design Best Practices - Anypoint Exchange for Sharing Assets - API-Led Connectivity Approach |
| Topic 8: Architecting and Deploying API Implementations | 11% | - High Availability (HA) Design - Mule Runtime Architecture - Disaster Recovery (DR) Strategies |
| Topic 9: Meeting API Quality Goals | 8% | - API Performance Optimization - Unit & Integration Testing - Resilience & Reliability Patterns |
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質問 # 13
A company requires Mule applications deployed to CloudHub to be isolated between non-production and production environments. This is so Mule applications deployed to non-production environments can only access backend systems running in their customer-hosted non-production environment, and so Mule applications deployed to production environments can only access backend systems running in their customer-hosted production environment. How does MuleSoft recommend modifying Mule applications, configuring environments, or changing infrastructure to support this type of per-environment isolation between Mule applications and backend systems?
正解：A
解説：
Correct Answe r: Create separate Anypoint VPCs for non-production and production environments, then configure connections to the backend systems in the corresponding customer-hosted environments.
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>> Creating different Business Groups does NOT make any difference w.r.t accessing the non-prod and prod customer-hosted environments. Still they will be accessing from both Business Groups unless process network restrictions are put in place.
>> We need to modify or couple the Mule Application Implementations with the environment. In fact, we should never implements application coupled with environments by binding them in the properties. Only basic things like endpoint URL etc should be bundled in properties but not environment level access restrictions.
>> IP addresses on CloudHub are dynamic until unless a special static addresses are assigned. So it is not possible to setup firewall rules in customer-hosted infrastrcture. More over, even if static IP addresses are assigned, there could be 100s of applications running on cloudhub and setting up rules for all of them would be a hectic task, non-maintainable and definitely got a good practice.
>> The best practice recommended by Mulesoft (In fact any cloud provider), is to have your Anypoint VPCs seperated for Prod and Non-Prod and perform the VPC peering or VPN tunneling for these Anypoint VPCs to respective Prod and Non-Prod customer-hosted environment networks.
Reference:
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質問 # 14
What is a best practice when building System APIs?
正解：C
解説：
Correct Answe r: Model all API resources and methods to closely mimic the operations of the backend system.
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>> There are NO fixed and straight best practices while opting data models for APIs. They are completly contextual and depends on number of factors. Based upon those factors, an enterprise can choose if they have to go with Enterprise Canonical Data Model or Bounded Context Model etc.
>> One should NEVER expose the technical details of API implementation to their API clients. Only the API interface/ RAML is exposed to API clients.
>> It is true that the RAML definitions of APIs should be as detailed as possible and should reflect most of the documentation. However, just that is NOT enough to call your API as best documented API. There should be even more documentation on Anypoint Exchange with API Notebooks etc. to make and create a developer friendly API and repository..
>> The best practice always when creating System APIs is to create their API interfaces by modeling their resources and methods to closely reflect the operations and functionalities of that backend system.
質問 # 15
A REST API is being designed to implement a Mule application.
What standard interface definition language can be used to define REST APIs?
正解：B
質問 # 16
Which three tools automate the deployment of Mule applications?
Choose 3 answers
正解：A、C、F
解説：
MuleSoft offers various tools to automate the deployment of Mule applications, which can streamline deployment and management processes. Here's how each tool supports automated deployment:
Runtime Manager:
Anypoint Runtime Manager is MuleSoft's web-based interface that allows users to deploy, manage, and monitor applications directly. It provides deployment automation through its user-friendly interface.
Anypoint Platform CLI:
The Anypoint CLI enables scripting of deployment and management tasks, making it possible to automate deployments via command-line scripts. This tool is ideal for CI/CD pipelines as it integrates with automated processes.
Platform APIs:
MuleSoft's Platform APIs allow programmatic access to deployment functions, enabling integration with external automation tools and CI/CD systems. These APIs facilitate deployment through RESTful calls, which can be automated for continuous delivery.
of Incorrect Options:
Option D (Anypoint Studio) is primarily for development and does not support deployment automation.
Option E (Maven Plugin) can be used for building and deploying Mule applications but isn't classified as a platform tool for deployment.
Option F (API Community Manager) is unrelated to deployment and instead focuses on managing API communities.
Reference
For detailed steps on automating deployments with these tools, refer to MuleSoft documentation on Runtime Manager, CLI, and Platform APIs.
質問 # 17
A customer has an ELA contract with MuleSoft. An API deployed to CloudHub is consistently experiencing performance issues. Based on the root cause analysis, it is determined that autoscaling needs to be applied.
How can this be achieved?
正解：C
解説：
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.
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.
Reference
For more on CloudHub's autoscaling configuration, refer to MuleSoft documentation on CloudHub autoscaling policies.
質問 # 18
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