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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Managing APIs | 12% | - API policies and security enforcement - API lifecycle management - Rate limiting and throttling - Versioning and deprecation strategies |
| Topic 2: Applying Integration Patterns | 11% | - Error handling and reliability patterns - Scalability and performance patterns - Common integration patterns and use cases - Event-driven and synchronous integration |
| Topic 3: Deploying API Implementations to CloudHub | 11% | - CloudHub architecture and capabilities - Worker sizing and resource planning - VPC and private space configuration - Deployment optimization and scaling |
| Topic 4: Establishing Organizational and Platform Foundations | 17% | - Platform strategy and roadmap definition - Center for Enablement (C4E) operating model - Governance and organizational structure - Anypoint Platform architecture and components |
| Topic 5: Monitoring and Analyzing Application Networks | 8% | - Analytics and insight generation - Operational visibility and optimization - Monitoring strategies and tools - Logging and alerting configuration |
| Topic 6: Meeting API Quality Goals | 8% | - Security and compliance standards - Reliability and availability targets - Maintainability and testability - Performance and latency requirements |
| Topic 7: Architecting and Deploying API Implementations | 11% | - Runtime architecture and deployment options - High availability and fault tolerance - CI/CD and DevOps integration - Networking and security configuration |
| Topic 8: Designing and Sharing APIs | 11% | - API layering: Experience, Process, System APIs - API design standards and best practices - Asset sharing and reuse via Anypoint Exchange - API specification and documentation |
| Topic 9: Explaining Application Network Basics | 11% | - Core concepts of application networks - Benefits of modern API design - API-led connectivity principles |
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NEW QUESTION # 112
What is most likely NOT a characteristic of an integration test for a REST API implementation?
Answer: D
Explanation:
Correct Answe r: The test runs immediately after the Mule application has been compiled and packaged
*****************************************
>> Integration tests are the last layer of tests we need to add to be fully covered.
>> These tests actually run against Mule running with your full configuration in place and are tested from external source as they work in PROD.
>> These tests exercise the application as a whole with actual transports enabled. So, external systems are affected when these tests run.
So, these tests do NOT run immediately after the Mule application has been compiled and packaged.
FYI... Unit Tests are the one that run immediately after the Mule application has been compiled and packaged.
NEW QUESTION # 113
A new upstream API Is being designed to offer an SLA of 500 ms median and 800 ms maximum (99th percentile) response time. The corresponding API implementation needs to sequentially invoke 3 downstream APIs of very similar complexity.
The first of these downstream APIs offers the following SLA for its response time: median: 100 ms, 80th percentile: 500 ms, 95th percentile: 1000 ms.
If possible, how can a timeout be set in the upstream API for the invocation of the first downstream API to meet the new upstream API's desired SLA?
Answer: A
Explanation:
Correct Answe r: Set a timeout of 100ms; that leaves 400ms for other two downstream APIs to complete
*****************************************
Key details to take from the given scenario:
>> Upstream API's designed SLA is 500ms (median). Lets ignore maximum SLA response times.
>> This API calls 3 downstream APIs sequentially and all these are of similar complexity.
>> The first downstream API is offering median SLA of 100ms, 80th percentile: 500ms; 95th percentile: 1000ms.
Based on the above details:
>> We can rule out the option which is suggesting to set 50ms timeout. Because, if the median SLA itself being offered is 100ms then most of the calls are going to timeout and time gets wasted in retried them and eventually gets exhausted with all retries. Even if some retries gets successful, the remaining time wont leave enough room for 2nd and 3rd downstream APIs to respond within time.
>> The option suggesting to NOT set a timeout as the invocation of this API is mandatory and so we must wait until it responds is silly. As not setting time out would go against the good implementation pattern and moreover if the first API is not responding within its offered median SLA 100ms then most probably it would either respond in 500ms (80th percentile) or 1000ms (95th percentile). In BOTH cases, getting a successful response from 1st downstream API does NO GOOD because already by this time the Upstream API SLA of 500 ms is breached. There is no time left to call 2nd and 3rd downstream APIs.
>> It is NOT true that no timeout is possible to meet the upstream APIs desired SLA.
As 1st downstream API is offering its median SLA of 100ms, it means MOST of the time we would get the responses within that time. So, setting a timeout of 100ms would be ideal for MOST calls as it leaves enough room of 400ms for remaining 2 downstream API calls.
NEW QUESTION # 114
A company deployed an API to a single worker/replica in the shared cloud in the U.S. West Region.
What happens when the Availability Zone experiences an outage?
Answer: D
Explanation:
In a CloudHub deployment with a single worker/replica located in a specific Availability Zone (AZ), if an AZ experiences an outage, here's what happens:
Worker Availability: Since the application is deployed in a single AZ, CloudHub does not automatically redeploy the application in a different zone or region during an outage. Thus, if the current AZ is unavailable, the application will be offline.
Auto-Restart upon AZ Recovery: Once the affected AZ is back online, CloudHub will auto-restart the worker in the same AZ without manual intervention. This ensures that as soon as the AZ is functional, the application resumes automatically.
of Correct Answer (B):
Option B accurately describes the situation, as the API will remain unavailable until the original AZ is restored.
CloudHub does not currently support automatic failover across regions or other availability zones within the same region for single-worker deployments on the shared cloud.
of Incorrect Options:
Option A (auto-redeployment in the U.S. East region) is incorrect, as CloudHub does not migrate across regions automatically.
Option C (redeployment in another AZ within the U.S. West) is not a feature for single-worker deployments.
Option D (manual redeployment triggered by an admin) is unnecessary as CloudHub handles restarts automatically when the AZ is back online.
Reference
Refer to MuleSoft CloudHub's availability and disaster recovery documentation for more information on how CloudHub manages availability in shared environments.
NEW QUESTION # 115
Which out-of-the-box key performance indicator measures the success of a typical Center for Enablement and is immediately available in responses from Anypoint Platform APIs?
Answer: C
Explanation:
Center for Enablement (C4E) KPIs:
A Center for Enablement (C4E) in MuleSoft focuses on enabling self-service and reuse by providing APIs that can be consumed across the organization. A key metric of success is how many consumers are utilizing the published APIs.
The number of consumers who have requested and received access to an API indicates the level of adoption and reuse, which aligns with the goals of a C4E.
Evaluating the Options:
Option A: This metric could indicate deployment automation, but it is not a direct measure of C4E's success in enabling API reuse and consumption.
Option B: Bandwidth usage per API implementation provides insight into API traffic but does not measure C4E enablement or consumer engagement.
Option C: The number of developers downloading an API specification can be an indicator of interest but does not confirm actual usage or enablement.
Option D (Correct Answer): The number of consumers who have requested and received access to an API in production is a key metric indicating API adoption and reuse, which aligns with C4E's goals.
Conclusion:
Option D is the correct answer as it provides a direct measure of consumer engagement and adoption, indicating the success of the C4E in promoting API usage across the organization.
Refer to MuleSoft's documentation on C4E KPIs and API usage metrics for additional insights.
NEW QUESTION # 116
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 # 117
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