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| Section | Weight | Objectives |
|---|---|---|
| Designing and Sharing APIs | 11% | - API Design Best Practices - Anypoint Exchange for Sharing Assets - API-Led Connectivity Approach |
| Monitoring and Analyzing Application Networks | 8% | - Analytics & Business Insights - Alerting & Auditing - Anypoint Monitoring Tools |
| Deploying API Implementations to CloudHub | 11% | - CloudHub Architecture - CloudHub 2.0 & Runtime Fabric - Deployment Strategies |
| Managing APIs | 12% | - API Security (Policies, SLAs) - API Lifecycle Management - API Versioning |
| Understanding the MuleSoft Platform & Application Networks | 11% | - Application Network Fundamentals - MuleSoft Anypoint Platform Core Concepts - C4E (Center for Enablement) Definition |
| Architecting and Deploying API Implementations | 11% | - Disaster Recovery (DR) Strategies - High Availability (HA) Design - Mule Runtime Architecture |
| Meeting API Quality Goals | 8% | - Resilience & Reliability Patterns - Unit & Integration Testing - API Performance Optimization |
| Applying Integration Patterns | 11% | - Batch Processing - Integration Scenarios & Patterns - Event-Driven Architecture (EDA) |
| Establishing Organizational and Platform Foundations | 17% | - Governance and Compliance Framework - IT Delivery & C4E Operating Model - Platform Foundation Setup |
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NEW QUESTION # 56
What is the most performant out-of-the-box solution in Anypoint Platform to track transaction state in an asynchronously executing long-running process implemented as a Mule application deployed to multiple CloudHub workers?
Answer: C
Explanation:
Correct Answe r: 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.
NEW QUESTION # 57
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: B
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 # 58
Which APIs can be used with DataGraph to create a unified schema?
Answer: A
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 # 59
A retail company is using an Order API to accept new orders. The Order API uses a JMS queue to submit orders to a backend order management service. The normal load for orders is being handled using two (2) CloudHub workers, each configured with 0.2 vCore. The CPU load of each CloudHub worker normally runs well below 70%. However, several times during the year the Order API gets four times (4x) the average number of orders. This causes the CloudHub worker CPU load to exceed 90% and the order submission time to exceed 30 seconds. The cause, however, is NOT the backend order management service, which still responds fast enough to meet the response SLA for the Order API. What is the MOST resource-efficient way to configure the Mule application's CloudHub deployment to help the company cope with this performance challenge?
Answer: C
Explanation:
Correct Answe r: Use a horizontal CloudHub autoscaling policy that triggers on CPU utilization greater than 70%
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The scenario in the question is very clearly stating that the usual traffic in the year is pretty well handled by the existing worker configuration with CPU running well below 70%. The problem occurs only "sometimes" occasionally when there is spike in the number of orders coming in.
So, based on above, We neither need to permanently increase the size of each worker nor need to permanently increase the number of workers. This is unnecessary as other than those "occasional" times the resources are idle and wasted.
We have two options left now. Either to use horizontal Cloudhub autoscaling policy to automatically increase the number of workers or to use vertical Cloudhub autoscaling policy to automatically increase the vCore size of each worker.
Here, we need to take two things into consideration:
1. CPU
2. Order Submission Rate to JMS Queue
>> From CPU perspective, both the options (horizontal and vertical scaling) solves the issue. Both helps to bring down the usage below 90%.
>> However, If we go with Vertical Scaling, then from Order Submission Rate perspective, as the application is still being load balanced with two workers only, there may not be much improvement in the incoming request processing rate and order submission rate to JMS queue. The throughput would be same as before. Only CPU utilization comes down.
>> But, if we go with Horizontal Scaling, it will spawn new workers and adds extra hand to increase the throughput as more workers are being load balanced now. This way we can address both CPU and Order Submission rate.
Hence, Horizontal CloudHub Autoscaling policy is the right and best answer.
NEW QUESTION # 60
A Mule 4 API has been deployed to CloudHub and a Basic Authentication - Simple policy has been applied to all API methods and resources. However, the API is still accessible by clients without using authentication.
How is this possible?
Answer: B
Explanation:
When a Basic Authentication policy is applied to an API on CloudHub but clients can still access the API without authentication, the likely cause is a missing Autodiscovery element. Here's how this affects API security:
Autodiscovery in MuleSoft:
The Autodiscovery element is essential for linking an API implementation deployed in CloudHub with its API instance defined in API Manager. This connection allows the policies applied in API Manager, such as Basic Authentication, to be enforced on the deployed API.
Why Option B is Correct:
Without Autodiscovery, the deployed application does not "know" about the policies configured in API Manager, resulting in unrestricted access. Adding Autodiscovery enables the API to enforce the policies correctly.
of Incorrect Options:
Option A (incorrect Exchange version) would not cause bypassing of security policies.
Option C (missing client applications) does not impact authentication policy enforcement.
Option D (worker restart) is irrelevant to policy enforcement.
Reference
Refer to MuleSoft documentation on Autodiscovery configuration and linking API Manager policies for additional information on setting up secure API policies.
NEW QUESTION # 61
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