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A code-centric API documentation environment should allow API consumers to investigate and execute API client source code that demonstrates invoking one or more APIs as part of representative scenarios. What is the most effective way to provide this type of code-centric API documentation environment using Anypoint Platform?
A. Enable mocking services for each of the relevant APIs and expose them via their Anypoint Exchange entry
B. Ensure the APIs are well documented through their Anypoint Exchange entries and API Consoles and share these pages with all API consumers
C. Create API Notebooks and include them in the relevant Anypoint Exchange entries
D. Make relevant APIs discoverable via an Anypoint Exchange entry
Explanation
Correct Answer: Create API Notebooks and Include them in the relevant Anypoint exchange entries
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API Notebooks are the one on Anypoint Platform that enable us to provide code-centric API documentation
Reference: [: https://docs.mulesoft.com/exchange/to-use-api-notebook, , Bottom of Form, Top of Form, , ]
In an organization, the InfoSec team is investigating Anypoint Platform related data traffic. From where does most of the data available to Anypoint Platform for monitoring and alerting originate?
A. From the Mule runtime or the API implementation, depending on the deployment model
B. From various components of Anypoint Platform, such as the Shared Load Balancer, VPC, and Mule runtimes
C. From the Mule runtime or the API Manager, depending on the type of data
D. From the Mule runtime irrespective of the deployment model
Explanation
Correct Answer: From the Mule runtime irrespective of the deployment model
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Monitoring and Alerting metrics are always originated from Mule Runtimes irrespective of the deployment model.
It may seems that some metrics (Runtime Manager) are originated from Mule Runtime and some are (API Invocations/ API Analytics) from API Manager. However, this is realistically NOT TRUE. The reason is, API manager is just a management tool for API instances but all policies upon applying on APIs eventually gets executed on Mule Runtimes only (Either Embedded or API Proxy).
Similarly all API Implementations also run on Mule Runtimes.
So, most of the day required for monitoring and alerts are originated fron Mule Runtimes only irrespective of whether the deployment model is MuleSoft-hosted or Customer-hosted or Hybrid.
A Mule application exposes an HTTPS endpoint and is deployed to three CloudHub workers that do not use static IP addresses. The Mule application expects a high volume of client requests in short time periods. What is the most cost-effective infrastructure component that should be used to serve the high volume of client requests?
A. A customer-hosted load balancer
B. The CloudHub shared load balancer
C. An API proxy
D. Runtime Manager autoscaling
Explanation
Correct Answer: The CloudHub shared load balancer
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The scenario in this question can be split as below:
There are 3 CloudHub workers (So, there are already good number of workers to handle high volume of requests)
The workers are not using static IP addresses (So, one CANNOT use customer load-balancing solutions without static IPs)
Looking for most cost-effective component to load balance the client requests among the workers.
Based on the above details given in the scenario:
Runtime autoscaling is NOT at all cost-effective as it incurs extra cost. Most over, there are already 3 workers running which is a good number.
We cannot go for a customer-hosted load balancer as it is also NOT most cost-effective (needs custom load balancer to maintain and licensing) and same time the Mule App is not having Static IP Addresses which limits from going with custom load balancing.
An API Proxy is irrelevant there as it has no role to play w.r.t handling high volumes or load balancing.
So, the only right option to go with and fits the purpose of scenario being most cost-effective is - using a CloudHub Shared Load Balancer.
What best explains the use of auto-discovery in API implementations?
A. It makes API Manager aware of API implementations and hence enables it to enforce policies
B. It enables Anypoint Studio to discover API definitions configured in Anypoint Platform
C. It enables Anypoint Exchange to discover assets and makes them available for reuse
D. It enables Anypoint Analytics to gain insight into the usage of APIs
Explanation:
Explanation
Correct Answer: It makes API Manager aware of API implementations and hence enables it
to enforce policies.
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>> API Autodiscovery is a mechanism that manages an API from API Manager by pairing
the deployed application to an API created on the platform.
>> API Management includes tracking, enforcing policies if you apply any, and reporting
API analytics.
>> Critical to the Autodiscovery process is identifying the API by providing the API name
and version.
References:
https://docs.mulesoft.com/api-manager/2.x/api-auto-discovery-new-concept
https://docs.mulesoft.com/api-manager/1.x/api-auto-discovery
https://docs.mulesoft.com/api-manager/2.x/api-auto-discovery-new-concept
When must an API implementation be deployed to an Anypoint VPC?
A. When the API Implementation must invoke publicly exposed services that are deployed outside of CloudHub in a customer- managed AWS instance
B. When the API implementation must be accessible within a subnet of a restricted customer-hosted network that does not allow public access
C. When the API implementation must be deployed to a production AWS VPC using the Mule Maven plugin
D. When the API Implementation must write to a persistent Object Store
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