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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Deploying API Implementations to CloudHub | 11% | - CloudHub architecture and capabilities - Deployment optimization and scaling - VPC and private space configuration - Worker sizing and resource planning |
| Topic 2: Managing APIs | 12% | - Rate limiting and throttling - API lifecycle management - Versioning and deprecation strategies - API policies and security enforcement |
| Topic 3: Meeting API Quality Goals | 8% | - Reliability and availability targets - Maintainability and testability - Performance and latency requirements - Security and compliance standards |
| Topic 4: Architecting and Deploying API Implementations | 11% | - Runtime architecture and deployment options - Networking and security configuration - High availability and fault tolerance - CI/CD and DevOps integration |
| Topic 5: Establishing Organizational and Platform Foundations | 17% | - Governance and organizational structure - Platform strategy and roadmap definition - Anypoint Platform architecture and components - Center for Enablement (C4E) operating model |
| Topic 6: Explaining Application Network Basics | 11% | - Core concepts of application networks - API-led connectivity principles - Benefits of modern API design |
| Topic 7: Monitoring and Analyzing Application Networks | 8% | - Analytics and insight generation - Logging and alerting configuration - Operational visibility and optimization - Monitoring strategies and tools |
| Topic 8: Designing and Sharing APIs | 11% | - Asset sharing and reuse via Anypoint Exchange - API specification and documentation - API layering: Experience, Process, System APIs - API design standards and best practices |
| Topic 9: Applying Integration Patterns | 11% | - Error handling and reliability patterns - Common integration patterns and use cases - Event-driven and synchronous integration - Scalability and performance patterns |
예를 들어Salesforce Mule-Arch-201 덤프를 보면 어떤 덤프제공사이트에서는 문항수가 아주 많은 자료를 제공해드리지만 저희Salesforce Mule-Arch-201덤프는 문항수가 적은 편입니다.왜냐하면 저희는 더 이상 출제되지 않는 오래된 문제들을 삭제해버리기 때문입니다. 문제가 많으면 고객들의 시간을 허비하게 됩니다. Itexamdump는 응시자에게 있어서 시간이 정말 소중하다는 것을 잘 알고 있습니다.
질문 # 151
What Mule application can have API policies applied by
Anypoint Platform to the endpoint exposed by that Mule application?
A) A Mule application that accepts requests over HTTP/1.x
B) A Mule application that accepts JSON requests over TCP but is NOT required to provide a response C) A Mute application that accepts JSON requests over WebSocket D) A Mule application that accepts gRPC requests over HTTP/2
정답：A
설명：
Correct Answe r: Option A
*****************************************
>> Anypoint API Manager and API policies are applicable to all types of HTTP/1.x APIs.
>> They are not applicable to WebSocket APIs, HTTP/2 APIs and gRPC APIs
질문 # 152
What is the main change to the IT operating model that MuleSoft recommends to organizations to improve innovation and clock speed?
정답：A
설명：
Correct Answe r: Drive consumption as much as production of assets; this enables developers to discover and reuse assets from other projects and encourages standardization
*****************************************
>> The main motto of the new IT Operating Model that MuleSoft recommends and made popular is to change the way that they are delivered from a production model to a production + consumption model, which is done through an API strategy called API-led connectivity.
>> The assets built should also be discoverable and self-serveable for reusablity across LOBs and organization.
>> MuleSoft's IT operating model does not talk about SDLC model (Agile/ Lean etc) or MDM at all. So, options suggesting these are not valid.
Reference:
https://blogs.mulesoft.com/biz/connectivity/what-is-a-center-for-enablement-c4e/
https://www.mulesoft.com/resources/api/secret-to-managing-it-projects
질문 # 153
What is true about the technology architecture of Anypoint VPCs?
정답：B
설명：
Correct Answe r: Traffic between Mule applications deployed to an Anypoint VPC and on-premises systems can stay within a private network
*****************************************
>> The private IP address range of an Anypoint VPC is NOT automatically chosen by CloudHub. It is chosen by us at the time of creating VPC using thr CIDR blocks.
CIDR Block: The size of the Anypoint VPC in Classless Inter-Domain Routing (CIDR) notation.
For example, if you set it to 10.111.0.0/24, the Anypoint VPC is granted 256 IP addresses from 10.111.0.0 to 10.111.0.255.
Ideally, the CIDR Blocks you choose for the Anypoint VPC come from a private IP space, and should not overlap with any other Anypoint VPC's CIDR Blocks, or any CIDR Blocks in use in your corporate network.
that each CloudHub environment requires a separate Anypoint VPC. Once an Anypoint VPC is created, we can choose a same VPC by multiple environments. However, it is generally a best and recommended practice to always have seperate Anypoint VPCs for Non-Prod and Prod environments.
>> We use Anypoint VPN to link the underlying AWS VPC to an on-premises (non AWS) private network. NOT VPC Peering.
Reference:
Only true statement in the given choices is that the traffic between Mule applications deployed to an Anypoint VPC and on-premises systems can stay within a private network.
https://docs.mulesoft.com/runtime-manager/vpc-connectivity-methods-concept
질문 # 154
A Platinum customer uses the U.S. control plane and deploys applications to CloudHub in Singapore with a default log configuration.
The compliance officer asks where the logs and monitoring data reside?
정답：A
설명：
For applications deployed on CloudHub in a foreign region (e.g., Singapore), MuleSoft handles log and monitoring data in the region where the control plane resides. This data storage policy is standard for CloudHub deployments to maintain centralized log and monitoring data.
Data Location:
For a U.S.-based control plane, all logs and monitoring data are stored in the United States, regardless of the deployment region.
Although the application itself runs in Singapore, data related to application performance and logs is not localized to the deployment region.
of Correct Answer (B):
Since the control plane is based in the United States, all operational data like logs and monitoring will also be stored there, ensuring compliance with MuleSoft's data handling policies.
of Incorrect Options:
Option A and D are incorrect because MuleSoft does not store logs or monitoring data in the application deployment region when the control plane is located in the United States.
Option C suggests mixed storage, which does not align with MuleSoft's data policy structure.
Reference
For details on data residency in CloudHub deployments, refer to MuleSoft's documentation on CloudHub control planes and data handling policies.
질문 # 155
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?
정답：D
설명：
Correct Answe r: Use a horizontal CloudHub autoscaling policy that triggers on CPU utilization greater than 70%
*****************************************
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.
질문 # 156
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