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| Section | Weight | Objectives |
|---|---|---|
| Monitoring and Analyzing Application Networks | 8% | - Analytics & Business Insights - Alerting & Auditing - Anypoint Monitoring Tools |
| Managing APIs | 12% | - API Lifecycle Management - API Security (Policies, SLAs) - API Versioning |
| Designing and Sharing APIs | 11% | - Anypoint Exchange for Sharing Assets - API Design Best Practices - API-Led Connectivity Approach |
| Establishing Organizational and Platform Foundations | 17% | - IT Delivery & C4E Operating Model - Governance and Compliance Framework - Platform Foundation Setup |
| Understanding the MuleSoft Platform & Application Networks | 11% | - C4E (Center for Enablement) Definition - MuleSoft Anypoint Platform Core Concepts - Application Network Fundamentals |
| Architecting and Deploying API Implementations | 11% | - High Availability (HA) Design - Mule Runtime Architecture - Disaster Recovery (DR) Strategies |
| Meeting API Quality Goals | 8% | - Resilience & Reliability Patterns - Unit & Integration Testing - API Performance Optimization |
| Applying Integration Patterns | 11% | - Event-Driven Architecture (EDA) - Batch Processing - Integration Scenarios & Patterns |
| Deploying API Implementations to CloudHub | 11% | - Deployment Strategies - CloudHub Architecture - CloudHub 2.0 & Runtime Fabric |
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NEW QUESTION # 130
What API policy would be LEAST LIKELY used when designing an Experience API that is intended to work with a consumer mobile phone or tablet application?
Answer: B
Explanation:
Correct Answe r: IP whitelist
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>> OAuth 2.0 access token and Client ID enforcement policies are VERY common to apply on Experience APIs as API consumers need to register and access the APIs using one of these mechanisms
>> JSON threat protection is also VERY common policy to apply on Experience APIs to prevent bad or suspicious payloads hitting the API implementations.
>> IP whitelisting policy is usually very common in Process and System APIs to only whitelist the IP range inside the local VPC. But also applied occassionally on some experience APIs where the End User/ API Consumers are FIXED.
>> When we know the API consumers upfront who are going to access certain Experience APIs, then we can request for static IPs from such consumers and whitelist them to prevent anyone else hitting the API.
However, the experience API given in the question/ scenario is intended to work with a consumer mobile phone or tablet application. Which means, there is no way we can know all possible IPs that are to be whitelisted as mobile phones and tablets can so many in number and any device in the city/state/country/globe.
So, It is very LEAST LIKELY to apply IP Whitelisting on such Experience APIs whose consumers are typically Mobile Phones or Tablets.
NEW QUESTION # 131
What should be ensured before sharing an API through a public Anypoint Exchange portal?
Answer: C
Explanation:
Correct Answe r: The visibility level of the API instances of that API that need to be publicly accessible should be set to public visibility.
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Reference:
https://docs.mulesoft.com/exchange/to-share-api-asset-to-portal
NEW QUESTION # 132
What Mule application deployment scenario requires using Anypoint Platform Private Cloud Edition or Anypoint Platform for Pivotal Cloud Foundry?
Answer: C
Explanation:
Correct Answe r: When regulatory requirements mandate on-premises processing of EVERY data item, including meta-data.
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We need NOT require to use Anypoint Platform PCE or PCF for the below. So these options are OUT.
>> We can make ALL applications highly available across multiple data centers using CloudHub too.
>> We can use Anypoint VPN and tunneling from CloudHub to connect to ALL backend systems in the application network that are deployed in the organization's intranet.
>> We can use Anypoint VPC and Firewall Rules to make ALL APIs private and NOT exposed to the public cloud.
Only valid reason in the given options that requires to use Anypoint Platform PCE/ PCF is - When regulatory requirements mandate on-premises processing of EVERY data item, including meta-data.
NEW QUESTION # 133
A large company wants to implement IT infrastructure in its own data center, based on the corporate IT policy requirements that data and metadata reside locally.
Which combination of Mule control plane and Mule runtime plane(s) meets the requirements?
Answer: D
Explanation:
Understanding Control and Runtime Planes:
Control Plane: The control plane is responsible for managing, monitoring, and deploying Mule applications. In a Private Cloud Edition (PCE), this control plane is deployed on-premises within the customer's infrastructure, meeting data residency and security requirements.
Runtime Plane: The runtime plane consists of Mule runtimes that execute Mule applications. By hosting these runtimes within the customer's infrastructure, data and metadata can remain local, which complies with corporate policies regarding data residency.
Evaluating the Options:
Option A: Using Anypoint Platform Private Cloud Edition for the control plane and the MuleSoft-hosted runtime plane would not meet the requirement, as the runtime plane is hosted by MuleSoft and would not keep data local.
Option B: The MuleSoft-hosted control plane with Anypoint Runtime Fabric for the runtime plane would still mean that metadata is managed in MuleSoft's cloud, which does not comply with the requirement to keep data and metadata on-premises.
Option C: A MuleSoft-hosted control plane and customer-hosted Mule runtimes also mean that metadata resides in the cloud, not on-premises, failing the residency requirement.
Option D (Correct Answer): Anypoint Platform Private Cloud Edition (PCE) for the control plane and customer-hosted Mule runtimes fulfill both requirements, as both the control plane and runtime plane would be hosted within the customer's data center.
Conclusion:
Option D is the correct answer, as it ensures that both the control plane and runtime plane are hosted on-premises, allowing data and metadata to reside locally per the corporate IT policy.
Refer to MuleSoft's documentation on Private Cloud Edition deployment and on-premise runtime configurations for further details.
NEW QUESTION # 134
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 # 135
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