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| Section | Weight | Objectives |
|---|---|---|
| Monitoring and Analyzing Application Networks | 8% | - Alerting & Auditing - Analytics & Business Insights - Anypoint Monitoring Tools |
| Managing APIs | 12% | - API Lifecycle Management - API Security (Policies, SLAs) - API Versioning |
| Architecting and Deploying API Implementations | 11% | - Mule Runtime Architecture - Disaster Recovery (DR) Strategies - High Availability (HA) Design |
| Designing and Sharing APIs | 11% | - Anypoint Exchange for Sharing Assets - API Design Best Practices - API-Led Connectivity Approach |
| Understanding the MuleSoft Platform & Application Networks | 11% | - MuleSoft Anypoint Platform Core Concepts - Application Network Fundamentals - C4E (Center for Enablement) Definition |
| Deploying API Implementations to CloudHub | 11% | - CloudHub Architecture - Deployment Strategies - CloudHub 2.0 & Runtime Fabric |
| Meeting API Quality Goals | 8% | - Unit & Integration Testing - Resilience & Reliability Patterns - API Performance Optimization |
| Applying Integration Patterns | 11% | - Event-Driven Architecture (EDA) - Batch Processing - Integration Scenarios & Patterns |
| Establishing Organizational and Platform Foundations | 17% | - Governance and Compliance Framework - Platform Foundation Setup - IT Delivery & C4E Operating Model |
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NEW QUESTION # 70
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?
Answer: A
Explanation:
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.
NEW QUESTION # 71
A large organization with an experienced central IT department is getting started using MuleSoft. There is a project to connect a siloed back-end system to a new Customer Relationship Management (CRM) system. The Center for Enablement is coaching them to use API-led connectivity.
What action would support the creation of an application network using API-led connectivity?
Answer: B
Explanation:
For an organization starting with API-led connectivity to integrate a siloed back-end system with a new CRM, the following approach aligns with best practices and MuleSoft's Center for Enablement (C4E) guidance:
API-led Connectivity: This model organizes APIs into distinct layers (System, Process, and Experience) to improve reusability, modularity, and manageability.
System APIs are used to expose and unlock data from core systems (such as back-end applications or databases).
Process APIs orchestrate data across multiple systems and transform it as needed.
Experience APIs format the data specifically for consumption by applications or devices, such as the CRM in this case.
Step to Support Application Network:
Create a System API for the back-end system. This API should expose the necessary data to support integration with the CRM.
By creating a System API with a RESTful interface, data can be accessed in a standardized way, making it easier to integrate with other systems and supporting future scalability.
Why Option D is Correct:
Creating a System API aligns with the principle of API-led connectivity, ensuring that data is exposed in a reusable manner. This API can then be orchestrated by Process APIs as needed to meet CRM requirements and can easily be extended to other applications.
of Incorrect Options:
Option A (creating a business process model) does not directly enable connectivity or expose back-end data through APIs.
Option B is unnecessary at this stage; assessing CRM capabilities like OAuth 2.0 support is not directly related to creating the application network via System APIs.
Option C contradicts API-led best practices by suggesting a point-to-point integration, which API-led connectivity seeks to avoid due to its lack of flexibility and scalability.
Reference
Refer to MuleSoft's API-led Connectivity resources for a detailed framework on building scalable integration layers using System, Process, and Experience APIs.
NEW QUESTION # 72
The application network is recomposable: it is built for change because it "bends but does not break"
Answer: A
Explanation:
*****************************************
>> Application Network is a disposable architecture.
>> Which means, it can be altered without disturbing entire architecture and its components.
>> It bends as per requirements or design changes but does not break
NEW QUESTION # 73
A system API is deployed to a primary environment as well as to a disaster recovery (DR) environment, with different DNS names in each environment. A process API is a client to the system API and is being rate limited by the system API, with different limits in each of the environments. The system API's DR environment provides only 20% of the rate limiting offered by the primary environment. What is the best API fault-tolerant invocation strategy to reduce overall errors in the process API, given these conditions and constraints?
Answer: B
Explanation:
Correct Answe r: Invoke the system API deployed to the primary environment; add timeout and retry logic to the process API to avoid intermittent failures; if it still fails, invoke the system API deployed to the DR environment
*****************************************
There is one important consideration to be noted in the question which is - System API in DR environment provides only 20% of the rate limiting offered by the primary environment. So, comparitively, very less calls will be allowed into the DR environment API opposed to its primary environment. With this in mind, lets analyse what is the right and best fault-tolerant invocation strategy.
1. Invoking both the system APIs in parallel is definitely NOT a feasible approach because of the 20% limitation we have on DR environment. Calling in parallel every time would easily and quickly exhaust the rate limits on DR environment and may not give chance to genuine intermittent error scenarios to let in during the time of need.
2. Another option given is suggesting to add timeout and retry logic to process API while invoking primary environment's system API. This is good so far. However, when all retries failed, the option is suggesting to invoke the copy of process API on DR environment which is not right or recommended. Only system API is the one to be considered for fallback and not the whole process API. Process APIs usually have lot of heavy orchestration calling many other APIs which we do not want to repeat again by calling DR's process API. So this option is NOT right.
3. One more option given is suggesting to add the retry (no timeout) logic to process API to directly retry on DR environment's system API instead of retrying the primary environment system API first. This is not at all a proper fallback. A proper fallback should occur only after all retries are performed and exhausted on Primary environment first. But here, the option is suggesting to directly retry fallback API on first failure itself without trying main API. So, this option is NOT right too.
This leaves us one option which is right and best fit.
- Invoke the system API deployed to the primary environment
- Add Timeout and Retry logic on it in process API
- If it fails even after all retries, then invoke the system API deployed to the DR environment.
NEW QUESTION # 74
Due to a limitation in the backend system, a system API can only handle up to 500 requests per second. What is the best type of API policy to apply to the system API to avoid overloading the backend system?
Answer: B
Explanation:
Correct Answe r: Spike control
*****************************************
>> First things first, HTTP Caching policy is for purposes different than avoiding the backend system from overloading. So this is OUT.
>> Rate Limiting and Throttling/ Spike Control policies are designed to limit API access, but have different intentions.
>> Rate limiting protects an API by applying a hard limit on its access.
>> Throttling/ Spike Control shapes API access by smoothing spikes in traffic.
That is why, Spike Control is the right option.
NEW QUESTION # 75
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