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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 |
| Architecting and Deploying API Implementations | 11% | - High Availability (HA) Design - Disaster Recovery (DR) Strategies - Mule Runtime Architecture |
| Applying Integration Patterns | 11% | - Integration Scenarios & Patterns - Batch Processing - Event-Driven Architecture (EDA) |
| Monitoring and Analyzing Application Networks | 8% | - Alerting & Auditing - Anypoint Monitoring Tools - Analytics & Business Insights |
| Understanding the MuleSoft Platform & Application Networks | 11% | - MuleSoft Anypoint Platform Core Concepts - Application Network Fundamentals - C4E (Center for Enablement) Definition |
| Managing APIs | 12% | - API Versioning - API Security (Policies, SLAs) - API Lifecycle Management |
| Meeting API Quality Goals | 8% | - Unit & Integration Testing - Resilience & Reliability Patterns - API Performance Optimization |
| Establishing Organizational and Platform Foundations | 17% | - Governance and Compliance Framework - IT Delivery & C4E Operating Model - Platform Foundation Setup |
| Deploying API Implementations to CloudHub | 11% | - Deployment Strategies - CloudHub 2.0 & Runtime Fabric - CloudHub Architecture |
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NEW QUESTION # 95
True or False. We should always make sure that the APIs being designed and developed are self-servable even if it needs more man-day effort and resources.
Answer: A
Explanation:
Correct Answe r: TRUE
*****************************************
>> As per MuleSoft proposed IT Operating Model, designing APIs and making sure that they are discoverable and self-servable is VERY VERY IMPORTANT and decides the success of an API and its application network.
NEW QUESTION # 96
Which statement is true about Spike Control policy and Rate Limiting policy?
Answer: C
Explanation:
Understanding Spike Control and Rate Limiting Policies:
Spike Control Policy: Limits the number of requests processed by the API in a short time to handle sudden bursts of traffic. It does not queue requests but rejects any request that exceeds the allowed burst rate.
Rate Limiting Policy: Sets a limit on the number of requests that an API can handle within a given timeframe. Once the limit is reached, additional requests are rejected.
Evaluating the Options:
Option A: Incorrect. In both Spike Control and Rate Limiting policies, requests are rejected once the limit is reached. Spike Control does not queue requests; it only controls the burst rate by rejecting excessive requests.
Option B (Correct Answer): In a clustered environment, each node independently enforces the Rate Limiting and Spike Control policies, meaning that the limits apply to each node separately. This ensures that each node can control its own resource usage independently within the cluster.
Option C: This is partially correct, as Rate Limiting is often used to protect Experience APIs, but Spike Control could also be useful in limiting resource consumption under high burst conditions.
Option D: Incorrect. Although a contract is required to enforce client-specific policies, Rate Limiting and Spike Control do not require a contract to function for general traffic control.
Conclusion:
Option B is the correct answer because, in a clustered environment, Rate Limiting and Spike Control policies apply separately to each node, helping each instance to manage its own load.
For more information, refer to MuleSoft's documentation on applying Rate Limiting and Spike Control policies in a clustered environment.
NEW QUESTION # 97
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 # 98
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 # 99
A company is using an on-prem cluster in the data center as a runtime plane and MuleSoft-hosted control plane.
How can the company monitor the detailed performance metrics on the Mule applications deployed to the cluster from the control plane?
Answer: A
Explanation:
Monitoring On-Premise Mule Applications:
For Mule applications deployed on an on-premises cluster, monitoring detailed performance metrics requires communication with the MuleSoft-hosted control plane. The control plane, when used with on-premises runtimes, relies on Anypoint Monitoring and requires a Monitoring Agent to gather and send detailed performance metrics.
Setting Up Monitoring:
To enable detailed metrics, the Monitoring Agent must be installed on each node in the cluster where Mule applications are deployed. This agent collects data on memory usage, CPU load, response times, and other metrics, and sends it to the control plane for aggregation and visualization.
Evaluating the Options:
Option A: Updating settings in the control plane alone does not enable detailed monitoring; the agent must be installed on each node to capture detailed metrics.
Option B (Correct Answer): Installing the Monitoring Agent on each node ensures that each runtime node in the cluster can send its metrics to the control plane, enabling detailed monitoring.
Option C: Installing the agent on a separate server would not be effective, as each node in the cluster needs to independently report its metrics to ensure full visibility.
Option D: The on-prem runtime does not automatically send detailed metrics to the control plane without the Monitoring Agent installed.
Conclusion:
Option B is the correct answer, as installing the Monitoring Agent on each node is essential for detailed performance monitoring of on-prem applications in a cluster.
Refer to MuleSoft's documentation on configuring Anypoint Monitoring for on-premises deployments and using the Monitoring Agent.
NEW QUESTION # 100
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