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NEW QUESTION # 47
Mule application A receives a request Anypoint MQ message REQU with a payload containing a variable-length list of request objects. Application A uses the For Each scope to split the list into individual objects and sends each object as a message to an Anypoint MQ queue.
Service S listens on that queue, processes each message independently of all other messages, and sends a response message to a response queue.
Application A listens on that response queue and must in turn create and publish a response Anypoint MQ message RESP with a payload containing the list of responses sent by service S in the same order as the request objects originally sent in REQU.
Assume successful response messages are returned by service S for all request messages.
What is required so that application A can ensure that the length and order of the list of objects in RESP and REQU match, while at the same time maximizing message throughput?
- A. Keep track of the list length and all object indices in REQU, both in the For Each scope and in all communication involving service Use persistent storage when creating RESP
- B. Perform all communication involving service S synchronously from within the For Each scope, so objects in RESP are in the exact same order as request objects in REQU
- C. Use a Scatter-Gather within the For Each scope to ensure response message order Configure the Scatter-Gather with a persistent object store
- D. Use an Async scope within the For Each scope and collect response messages in a second For Each scope in the order In which they arrive, then send RESP using this list of responses
Answer: A
Explanation:
Correct answer is Perform all communication involving service S synchronously from within the For Each scope, so objects in RESP are in the exact same order as request objects in REQU : Using Anypoint MQ, you can create two types of queues: Standard queue These queues don’t guarantee a specific message order. Standard queues are the best fit for applications in which messages must be delivered quickly. FIFO (first in, first out) queue These queues ensure that your messages arrive in order. FIFO queues are the best fit for applications requiring strict message ordering and exactly-once delivery, but in which message delivery speed is of less importance Use of FIFO queue is no where in the option and also it decreased throughput. Similarly persistent object store is not the preferred solution approach when you maximizing message throughput. This rules out one of the options. Scatter Gather does not support ObjectStore. This rules out one of the options. Standard Anypoint MQ queues don’t guarantee a specific message order hence using another for each block to collect response wont work as requirement here is to ensure the order. Hence considering all the above factors the feasible approach is Perform all communication involving service S synchronously from within the For Each scope, so objects in RESP are in the exact same order as request objects in REQU
NEW QUESTION # 48
An airline is architecting an API connectivity project to integrate its flight data into an online aggregation website. The interface must allow for secure communication high-performance and asynchronous message exchange.
What are suitable interface technologies for this integration assuming that Mulesoft fully supports these technologies and that Anypoint connectors exist for these interfaces?
- A. AsyncAPI over HTTPS
AMQP with RabbitMQ JSON/REST over HTTPS - B. SOAP over HTTPS HOP over TLS gRPC over HTTPS
- C. XML over ActiveMQ XML over SFTP XML/REST over HTTPS
- D. CSV over FTP YAM L over TLS JSON over HTTPS
Answer: A
NEW QUESTION # 49
Refer to the exhibit.
One of the backend systems invoked by an API implementation enforces rate limits on the number of requests a particular client can make. Both the backend system and the API implementation are deployed to several non-production environments in addition to production.
Rate limiting of the backend system applies to all non-production environments. The production environment, however, does NOT have any rate limiting.
What is the most effective approach to conduct performance tests of the API implementation in a staging (non-production) environment?
- A. Include logic within the API implementation that bypasses invocations of the backend system in a performance test situation. Instead invoking local stubs that replicate typical backend system responses then conduct performance tests using this API Implementation
- B. Use MUnit to simulate standard responses from the backend system then conduct performance tests to identify other bottlenecks in the system
- C. Create a mocking service that replicates the backend system’s production performance characteristics. Then configure the API implementation to use the mocking service and conduct the performance tests
- D. Conduct scaled-down performance tests in the staging environment against the rate limited backend system then upscale performance results to full production scale
Answer: C
Explanation:
Correct answer is Create a mocking service that replicates the backend system’s production performance characteristics. Then configure the API implementation to use the mocking service and conduct the performance tests
* MUnit is for only Unit and integration testing for APIs and Mule apps. Not for performance Testing, even if it has the ability to Mock the backend.
* Bypassing the backend invocation defeats the whole purpose of performance testing. Hence it is not a valid answer.
* Scaled down performance tests cant be relied upon as performance of API’s is not linear against load.
NEW QUESTION # {0}50
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