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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Deploying API Implementations to CloudHub | 11% | - CloudHub 2.0 & Runtime Fabric - CloudHub Architecture - Deployment Strategies |
| Topic 2: Monitoring and Analyzing Application Networks | 8% | - Alerting & Auditing - Analytics & Business Insights - Anypoint Monitoring Tools |
| Topic 3: Applying Integration Patterns | 11% | - Event-Driven Architecture (EDA) - Batch Processing - Integration Scenarios & Patterns |
| Topic 4: Managing APIs | 12% | - API Security (Policies, SLAs) - API Lifecycle Management - API Versioning |
| Topic 5: Establishing Organizational and Platform Foundations | 17% | - Governance and Compliance Framework - Platform Foundation Setup - IT Delivery & C4E Operating Model |
| Topic 6: Understanding the MuleSoft Platform & Application Networks | 11% | - MuleSoft Anypoint Platform Core Concepts - Application Network Fundamentals - C4E (Center for Enablement) Definition |
| Topic 7: Meeting API Quality Goals | 8% | - Resilience & Reliability Patterns - API Performance Optimization - Unit & Integration Testing |
| Topic 8: Designing and Sharing APIs | 11% | - API Design Best Practices - Anypoint Exchange for Sharing Assets - API-Led Connectivity Approach |
| Topic 9: Architecting and Deploying API Implementations | 11% | - High Availability (HA) Design - Mule Runtime Architecture - Disaster Recovery (DR) Strategies |
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NEW QUESTION # 140
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: B
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 # 141
Refer to the exhibit.
An organization uses one specific CloudHub (AWS) region for all CloudHub deployments.
How are CloudHub workers assigned to availability zones (AZs) when the organization's Mule applications are deployed to CloudHub in that region?
Answer: A
Explanation:
Correct Answe r: Workers are randomly distributed across available AZs within that region.
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>> Currently, we only have control to choose which AWS Region to choose but there is no control at all using any configurations or deployment options to decide what Availability Zone (AZ) to assign to what worker.
>> There are NO fixed or implicit rules on platform too w.r.t assignment of AZ to workers based on environment or application.
>> They are completely assigned in random. However, cloudhub definitely ensures that HA is achieved by assigning the workers to more than on AZ so that all workers are not assigned to same AZ for same application.
Reference:
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NEW QUESTION # 142
What is a typical result of using a fine-grained rather than a coarse-grained API deployment model to implement a given business process?
Answer: C
Explanation:
Correct Answe r: A higher number of discoverable API-related assets in the application network.
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>> We do NOT get faster response times in fine-grained approach when compared to coarse-grained approach.
>> In fact, we get faster response times from a network having coarse-grained APIs compared to a network having fine-grained APIs model. The reasons are below.
Fine-grained approach:
1. will have more APIs compared to coarse-grained
2. So, more orchestration needs to be done to achieve a functionality in business process.
3. Which means, lots of API calls to be made. So, more connections will needs to be established. So, obviously more hops, more network i/o, more number of integration points compared to coarse-grained approach where fewer APIs with bulk functionality embedded in them.
4. That is why, because of all these extra hops and added latencies, fine-grained approach will have bit more response times compared to coarse-grained.
5. Not only added latencies and connections, there will be more resources used up in fine-grained approach due to more number of APIs.
That's why, fine-grained APIs are good in a way to expose more number of resuable assets in your network and make them discoverable. However, needs more maintenance, taking care of integration points, connections, resources with a little compromise w.r.t network hops and response times.
NEW QUESTION # 143
Which of the following best fits the definition of API-led connectivity?
Answer: B
Explanation:
Correct Answe r: API-led connectivity is not just an architecture or technology but also a way to organize people and processes for efficient IT delivery in the organization.
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Reference:
NEW QUESTION # 144
Several times a week, an API implementation shows several thousand requests per minute in an Anypoint Monitoring dashboard, Between these bursts, the dashboard shows between two and five requests per minute. The API implementation is running on Anypoint Runtime Fabric with two non-clustered replicas, reserved vCPU 1.0 and vCPU Limit 2.0.
An API consumer has complained about slow response time, and the dashboard shows the 99 percentile is greater than 120 seconds at the time of the complaint. It also shows greater than 90% CPU usage during these time periods.
In manual tests in the QA environment, the API consumer has consistently reproduced the slow response time and high CPU usage, and there were no other API requests at this time. In a brainstorming session, the engineering team has created several proposals to reduce the response time for requests.
Which proposal should be pursued first?
Answer: C
Explanation:
Scenario Analysis:
The API implementation is experiencing high CPU usage (over 90%) during bursts of requests, which correlates with slow response times, as indicated by a 99th percentile response time greater than 120 seconds.
The API implementation is running on Anypoint Runtime Fabric with two non-clustered replicas and has a reserved vCPU of 1.0 and a vCPU limit of 2.0.
The high CPU usage during bursts suggests that the current resources may not be sufficient to handle peak loads.
Evaluating the Options:
Option A (Correct Answer): Increasing the vCPU resources for each replica would provide more processing power to handle high traffic volumes, potentially reducing the response time during spikes. Since the CPU usage is consistently high during bursts, this option directly addresses the resource bottleneck.
Option B: Modifying the API client to split requests may reduce individual request load but could be complex to implement on the client side and may not fully address the high CPU issue.
Option C: Increasing the number of replicas could help distribute the load; however, with a high CPU load on each replica, adding more replicas without increasing CPU resources may not fully resolve the problem.
Option D: Throttling the client would reduce the number of requests, but this may not be acceptable if the client needs to maintain a high request rate. It also does not directly address the CPU limitations of the API implementation.
Conclusion:
Option A is the best choice as it addresses the root cause of high CPU usage by increasing the vCPU allocation, allowing the API to handle more requests efficiently. This should be pursued first before considering other options.
Refer to MuleSoft's documentation on Runtime Fabric and vCPU resource allocation for more details on optimizing API performance in high-demand environments.
NEW QUESTION # 145
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