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問題 #1

A Rate Limiting policy is applied to an API implementation to protect the back-end system. Recently, there have been surges in demand that cause some API client POST requests to the API implementation to be rejected with policy-related errors, causing delays and complications to the API clients.
How should the API policies that are applied to the API implementation be changed to reduce the frequency of errors returned to API clients, while still protecting the back-end system?

A. Keep the Rate Limiting policy and add an SLA-based Spike Control policy
B. Keep the Rate Limiting policy and add 9 Client ID Enforcement policy
C. Remove the Rate Limiting policy and add an HTTP Caching policy
D. Remove the Rate Limiting policy and add a Spike Control policy

問題 #2

What should be ensured before sharing an API through a public Anypoint Exchange portal?

A. The visibility level of the API instances of that API that need to be publicly accessible should be set to public visibility
B. The API should be functional with at least an initial implementation deployed and accessible for users to interact with
C. The users needing access to the API should be added to the appropriate role in Anypoint Platform
D. The API should be secured using one of the supported authentication/authorization mechanisms to ensure that data is not compromised

問題 #3

What correctly characterizes unit tests of Mule applications?

A. They must be run in a unit testing environment with dedicated Mule runtimes for the environment
B. They are typically written using MUnit to run in an embedded Mule runtime that does not require external connectivity
C. They must be triggered by an external client tool or event source
D. They test the validity of input and output of source and target systems

問題 #4

What API policy would LEAST likely be applied to a Process API?

A. Custom circuit breaker
B. JSON threat protection
C. Rate limiting
D. Client ID enforcement

問題 #5

A retail company with thousands of stores has an API to receive data about purchases and insert it into a single database. Each individual store sends a batch of purchase data to the API about every 30 minutes. The API implementation uses a database bulk insert command to submit all the purchase data to a database using a custom JDBC driver provided by a data analytics solution provider. The API implementation is deployed to a single CloudHub worker. The JDBC driver processes the data into a set of several temporary disk files on the CloudHub worker, and then the data is sent to an analytics engine using a proprietary protocol. This process usually takes less than a few minutes. Sometimes a request fails. In this case, the logs show a message from the JDBC driver indicating an out-of-file-space message. When the request is resubmitted, it is successful. What is the best way to try to resolve this throughput issue?

A. se a CloudHub autoscaling policy to add CloudHub workers
B. Increase the size of the CloudHub worker(s)
C. Increase the number of CloudHub workers
D. Use a CloudHub autoscaling policy to increase the size of the CloudHub worker

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