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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 |
| Monitoring and Analyzing Application Networks | 8% | - Alerting & Auditing - Anypoint Monitoring Tools - Analytics & Business Insights |
| Architecting and Deploying API Implementations | 11% | - High Availability (HA) Design - Mule Runtime Architecture - Disaster Recovery (DR) Strategies |
| Applying Integration Patterns | 11% | - Batch Processing - Integration Scenarios & Patterns - Event-Driven Architecture (EDA) |
| Deploying API Implementations to CloudHub | 11% | - CloudHub 2.0 & Runtime Fabric - CloudHub Architecture - Deployment Strategies |
| Meeting API Quality Goals | 8% | - Resilience & Reliability Patterns - API Performance Optimization - Unit & Integration Testing |
| Managing APIs | 12% | - API Versioning - API Security (Policies, SLAs) - API Lifecycle Management |
| Establishing Organizational and Platform Foundations | 17% | - Governance and Compliance Framework - Platform Foundation Setup - IT Delivery & C4E Operating Model |
| Understanding the MuleSoft Platform & Application Networks | 11% | - MuleSoft Anypoint Platform Core Concepts - C4E (Center for Enablement) Definition - Application Network Fundamentals |
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NEW QUESTION # 14
An application updates an inventory running only one process at any given time to keep the inventory consistent. This process takes 200 milliseconds (.2 seconds) to execute; therefore, the scalability threshold of the application is five requests per second.
What is the impact on the application if horizontal scaling is applied, thereby increasing the number of Mule workers?
Answer: B
Explanation:
Given that the application is designed to handle only one process at a time to maintain data consistency, here's why horizontal scaling won't increase the processing limit:
Single-Process Constraint:
The application limits to processing one transaction at a time due to its design for consistency, meaning horizontal scaling (adding more workers) does not increase processing speed beyond this limit.
Execution Time:
Since each request takes 200 ms, five requests per second is the maximum processing threshold. Increasing the number of workers does not bypass this single-process limitation.
of Correct Answer (A):
The scalability remains at five requests per second, as this constraint is intrinsic to the application's design.
of Incorrect Options:
Option B suggests a change in execution time, which horizontal scaling does not affect.
Option C assumes doubling the throughput, which isn't possible due to the single-threaded nature of the application.
Option D suggests horizontal scaling cannot apply, which is incorrect; however, scaling does not increase throughput in this context.
Reference
For more on understanding scaling and concurrency in Mule applications, see MuleSoft's documentation on application performance and scaling limitations.
NEW QUESTION # 15
An organization has implemented a Customer Address API to retrieve customer address information. This API has been deployed to multiple environments and has been configured to enforce client IDs everywhere.
A developer is writing a client application to allow a user to update their address. The developer has found the Customer Address API in Anypoint Exchange and wants to use it in their client application.
What step of gaining access to the API can be performed automatically by Anypoint Platform?
Answer: D
Explanation:
Correct Answe r: Approve the client application request for the chosen SLA tier
*****************************************
>> Only approving the client application request for the chosen SLA tier can be automated
>> Rest of the provided options are not valid
NEW QUESTION # 16
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: A
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 # 17
An organization has built an application network following the API-led connectivity approach recommended by MuleSoft. To protect the application network against attacks from malicious external API clients, the organization plans to apply JSON Threat Protection policies.
To which API-led connectivity layer should the JSON Threat Protection policies most commonly be applied?
Answer: B
Explanation:
Understanding JSON Threat Protection Policies:
JSON Threat Protection policies are used to protect APIs from attacks that exploit JSON payloads, such as oversized payloads, deeply nested objects, and excessive array elements. This helps prevent Denial of Service (DoS) attacks and other malicious payload-related threats.
These policies are typically applied to safeguard APIs that are directly exposed to external clients, where the risk of receiving malicious payloads is highest.
API-led Connectivity Layers:
Experience Layer: This layer is designed to expose APIs to end-users or external API clients, often acting as the interface that interacts with users or applications.
Process Layer: This layer is used for orchestration and aggregation of data from various System APIs, typically operating within a trusted environment and not directly exposed to external clients.
System Layer: This layer provides access to backend systems and databases, often within the organization's secure environment and not directly accessible to external clients.
Evaluating the Options:
Option A (All layers): While JSON Threat Protection can technically be applied to all layers, it is most commonly applied at the Experience layer, where APIs are exposed to external traffic and are more vulnerable to attacks.
Option B (System layer): The System layer is generally not exposed to external clients directly, so JSON Threat Protection is less critical here.
Option C (Process layer): Similar to the System layer, the Process layer is typically internal and not exposed directly to external clients, so JSON Threat Protection is less commonly applied.
Option D (Correct Answer): The Experience layer is the correct answer because it is the layer that directly interacts with external clients, making it the primary target for malicious payloads. Applying JSON Threat Protection here effectively protects the application network from external threats.
Conclusion:
Option D is the correct answer, as the Experience layer is the most common layer for applying JSON Threat Protection policies to protect against external attacks.
For further reference, consult MuleSoft's documentation on API security policies and best practices for securing APIs at the Experience layer.
NEW QUESTION # 18
What Anypoint Connectors support transactions?
Answer: D
NEW QUESTION # 19
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