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| Section | Weight | Objectives |
|---|---|---|
| Establishing Organizational and Platform Foundations | 17% | - Governance and organizational structure - Center for Enablement (C4E) operating model - Anypoint Platform architecture and components - Platform strategy and roadmap definition |
| Applying Integration Patterns | 11% | - Scalability and performance patterns - Error handling and reliability patterns - Event-driven and synchronous integration - Common integration patterns and use cases |
| Managing APIs | 12% | - Rate limiting and throttling - Versioning and deprecation strategies - API policies and security enforcement - API lifecycle management |
| Designing and Sharing APIs | 11% | - API layering: Experience, Process, System APIs - Asset sharing and reuse via Anypoint Exchange - API specification and documentation - API design standards and best practices |
| Meeting API Quality Goals | 8% | - Performance and latency requirements - Maintainability and testability - Reliability and availability targets - Security and compliance standards |
| Architecting and Deploying API Implementations | 11% | - CI/CD and DevOps integration - High availability and fault tolerance - Runtime architecture and deployment options - Networking and security configuration |
| Explaining Application Network Basics | 11% | - Benefits of modern API design - API-led connectivity principles - Core concepts of application networks |
| Monitoring and Analyzing Application Networks | 8% | - Logging and alerting configuration - Operational visibility and optimization - Monitoring strategies and tools - Analytics and insight generation |
| Deploying API Implementations to CloudHub | 11% | - CloudHub architecture and capabilities - Worker sizing and resource planning - VPC and private space configuration - Deployment optimization and scaling |
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NEW QUESTION # 19
An enterprise is embarking on the API-led digital transformation journey, and the central IT team has started to define System APIs. Currently there is no Enterprise Data Model being defined within the enterprise, and the definition of a clean Bounded Context Data Model requires too much effort.
According to MuleSoft's recommended guidelines, how should the System API data model be defined?
Answer: A
Explanation:
When defining data models for System APIs without an established Enterprise Data Model, MuleSoft recommends mirroring the back-end systems' data types to achieve quick and effective integration without adding complexity. This approach has several benefits:
Alignment with Backend Systems:
Mirroring data types ensures consistency with backend data sources, which simplifies integration, reduces mapping requirements, and minimizes potential data transformation issues.
Flexibility for Future Enhancements:
By retaining close alignment with backend data structures, System APIs can evolve to support an Enterprise Data Model in the future without immediate restructuring.
of Incorrect Options:
Option A (exposing misspellings) is not recommended as System APIs should still ensure a professional and coherent interface.
Option C (custom naming) complicates the API structure without adding immediate value in the absence of a clear data model.
Option D (exposing all fields) is unnecessary and can reduce performance and add complexity.
Reference
Refer to MuleSoft best practices for data modeling in System APIs for additional information on mirroring backend systems.
NEW QUESTION # 20
An API implementation is updated. When must the RAML definition of the API also be updated?
Answer: D
Explanation:
Correct Answe r: When the API implementation changes the structure of the request or response messages
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>> RAML definition usually needs to be touched only when there are changes in the request/response schemas or in any traits on API.
>> It need not be modified for any internal changes in API implementation like performance tuning, backend system migrations etc..
NEW QUESTION # 21
To minimize operation costs, a customer wants to use a CloudHub 1.0 solution. The customer's requirements are:
* Separate resources with two Business groups
* High-availability (HA) for all APIs
* Route traffic via Dedicated load balancer (DLBs)
* Separate environments into production and non-production
Which solution meets the customer's needs?
Answer: A
Explanation:
Understanding the Requirements:
Business Groups: The solution must support two business groups, which typically require separate VPCs for logical separation.
High Availability (HA): Requires deploying resources across multiple availability zones.
Dedicated Load Balancer (DLB): Traffic should be routed via DLBs, which operate within VPCs on CloudHub.
Separate Environments: There needs to be separation between production and non-production environments.
Evaluating the Options:
Option A: Using a single production and non-production VPC and differentiating business groups via availability zones is not ideal as it does not provide full separation for each business group, and using maximum CIDR allocation is wasteful.
Option B (Correct Answer): Creating separate production and non-production VPCs per business group with minimized CIDR blocks, multiple availability zones, and multiple workers per application for HA meets all requirements effectively.
Option C: While this option separates VPCs per business group, it does not fully address the requirement for HA across availability zones by specifying multi-zone deployment only during API deployment, which may not guarantee redundancy.
Option D: Configuring subnets to differentiate business groups within a single production and non-production VPC does not fully separate the business groups, which is a requirement.
Conclusion:
Option B is the best choice as it meets the requirements for high availability, business group separation, and cost efficiency by using minimized CIDR allocations and deploying multiple workers across availability zones.
For further reference, refer to MuleSoft's documentation on VPC configuration and high availability deployment strategies.
NEW QUESTION # 22
What Mule application deployment scenario requires using Anypoint Platform Private Cloud Edition or Anypoint Platform for Pivotal Cloud Foundry?
Answer: A
Explanation:
Correct Answe r: When regulatory requirements mandate on-premises processing of EVERY data item, including meta-data.
*****************************************
We need NOT require to use Anypoint Platform PCE or PCF for the below. So these options are OUT.
>> We can make ALL applications highly available across multiple data centers using CloudHub too.
>> We can use Anypoint VPN and tunneling from CloudHub to connect to ALL backend systems in the application network that are deployed in the organization's intranet.
>> We can use Anypoint VPC and Firewall Rules to make ALL APIs private and NOT exposed to the public cloud.
Only valid reason in the given options that requires to use Anypoint Platform PCE/ PCF is - When regulatory requirements mandate on-premises processing of EVERY data item, including meta-data.
NEW QUESTION # 23
Which APIs can be used with DataGraph to create a unified schema?
Answer: C
Explanation:
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.
Reference
For 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.
NEW QUESTION # 24
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