Examice

Exam MCIA - Level 1 All QuestionsBrowse all questions from this exam

Question 14

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?

Use MUnit to simulate standard responses from the backend system Then conduct performance tests to identify other bottlenecks in the system

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

Conduct scaled-down performance tests in the staging environment against the rate limited backend system Then upscale performance results to full production scale

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

Correct Answer: B

To conduct effective performance tests in a non-production environment that has rate limits, creating a mocking service that replicates the backend system's production performance characteristics is the best approach. This allows the API implementation to be tested under conditions that closely match the production environment, without being hindered by the rate limits. This method ensures that the performance tests realistically simulate the actual production environment, providing accurate performance metrics.

Discussion

Pavan_NagineniOption: B

B. 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

KazzmanOption: B

Correct answer should be B

sanni27Option: C

Should be C, because the scale down performance test always happens in production identical environment. Here it is staging environment.

AlandtOption: B

ChatGPT: The most effective approach is: B. 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. This approach ensures realistic performance testing by bypassing non-production rate limits, simulating production-like conditions, and providing control and flexibility to accurately assess the API implementation's performance.

vikas_muleOption: C

C is correct, The production environment, however, does NOT have any rate limiting. so no need to conduct rate limiting performance test in staging.

anupamgogoiOption: C

It should be C. We should have a PRODLIKE environment where we can test the functionality. Mock is not a good idea

godricravenclawOption: C

D requires a code change which is not advisable because it will not be deployed in production and will affect the performance results (even though it might be milliseconds only).

majda091983Option: C

could not be B because it implies to touch the API implementation

madgeezerOption: B

rodriguescontOption: B

Sounds B, but I just not figured out how the mocking service feature could help regarding the lack of rating limit on the backend in production

godricravenclaw

Agree that it is B. As for the mocking service, it will behave like in production since the mocking service will not have rate limiting.

Outdoor25Option: B

Should be B.