Salesforce MuleSoft-Platform-Architect-I - Salesforce Certified MuleSoft Platform Architect 1 Exam (SP25)

Explanation

Correct Answer: Unit tests (Whitebox)

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Explanation

Correct Answer: Set a timeout of 100ms; that leaves 400ms for other two downstream APIs to complete

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Key details to take from the given scenario:

>> Upstream API's designed SLA is 500ms (median). Lets ignore maximum SLA response times.

>> This API calls 3 downstream APIs sequentially and all these are of similar complexity.

>> The first downstream API is offering median SLA of 100ms, 80th percentile: 500ms; 95th percentile: 1000ms.

Based on the above details:

>> We can rule out the option which is suggesting to set 50ms timeout. Because, if the median SLA itself being offered is 100ms then most of the calls are going to timeout and time gets wasted in retried them and eventually gets exhausted with all retries. Even if some retries gets successful, the remaining time wont leave enough room for 2nd and 3rd downstream APIs to respond within time.

>> The option suggesting to NOT set a timeout as the invocation of this API is mandatory and so we must wait until it responds is silly. As not setting time out would go against the good implementation pattern and moreover if the first API is not responding within its offered median SLA 100ms then most probably it would either respond in 500ms (80th percentile) or 1000ms (95th percentile). In BOTH cases, getting a successful response from 1st downstream API does NO GOOD because already by this time the Upstream API SLA of 500 ms is breached. There is no time left to call 2nd and 3rd downstream APIs.

>> It is NOT true that no timeout is possible to meet the upstream APIs desired SLA.

As 1st downstream API is offering its median SLA of 100ms, it means MOST of the time we would get the responses within that time. So, setting a timeout of 100ms would be ideal for MOST calls as it leaves enough room of 400ms for remaining 2 downstream API calls.

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.

To accommodate the new requirement of allowing updates to existing quotes, the following actions should be taken:

Update the RAML Definition (Option C):

The RAML specification defines the structure and behavior of the API. Adding a new method (such as PUT or PATCH) for updating quotes requires modifying the RAML to include this new endpoint. This ensures the API specification is up-to-date and accurately reflects the new functionality.

Update the API Implementation (Option A):

Once the RAML is updated, the backend API implementation must also be modified to handle the new update requests. This could involve adding logic to process and validate update requests, connect to necessary backend resources, and apply the changes to existing quotes.

Explanation of Incorrect Options:

Option B (removing and creating new clients) is unnecessary; client applications can remain as they are, with no need for complete replacement.

Option D (deprecating existing versions) may not be required if backward compatibility is maintained.

Option E (adding a new policy) does not facilitate functional changes and is unrelated to implementing the update feature.

ReferencesFor more details on updating RAML definitions and API implementations, refer to MuleSoft’s API Design documentation on RAML and RESTful API practices​.

Explanation

Correct Answer: Data on past API invocations to help identify anomalies and usage patterns across various APIs

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API Invocation metrics provided by Anypoint Platform:

>> Does NOT provide any Return Of Investment (ROI) related information. So the option suggesting it is OUT.

>> Does NOT provide any information w.r.t how APIs are reused, whether there is effective usage of APIs or not etc...

>> Does NOT prodive any prediction information as such to help us proactively identify any future policy violations.

So, the kind of data/information we can get from such metrics is on past API invocations to help identify anomalies and usage patterns across various APIs.

Explanation

Correct Answer: TRUE

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>> As per MuleSoft proposed IT Operating Model, designing APIs and making sure that they are discoverable and self-servable is VERY VERY IMPORTANT and decides the success of an API and its application network.

Explanation

Correct Answer: 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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Explanation

Correct Answer: Build several Bounded Context Data Models that align with coherent parts of the business processes and the definitions of associated business entities.

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>> The options w.r.t building API data models using XML schema/ Agile API-centric practices are irrelevant to the scenario given in the question. So these two are INVALID.

>> Building EDM (Enterprise Data Model) is not feasible or right fit for this scenario as the teams and LOBs work in silo and they all have different initiatives, budget etc.. Building EDM needs intensive coordination among all the team which evidently seems not possible in this scenario.

So, the right fit for this scenario is to build several Bounded Context Data Models that align with coherent parts of the business processes and the definitions of associated business entities.

Understanding the API Flow and Timeout Requirement:

The Order API initiates a call to the OrderItems process API, which in turn calls the Inventory system API to fetch details from the inventory database.

The requirement specifies that the Order API should not wait more than two seconds for a response from the OrderItems process API, even if there are delays further down the chain (between Inventory system API and the database).

Choosing the Appropriate Timeout Location:

Setting the timeout at the OrderItems process API level ensures that if the Inventory system API takes longer than two seconds to respond, the OrderItems process API will terminate the request and send a timeout response back to the Order API. This prevents the Order API from waiting indefinitely due to the unreliable connection to the database.

If the timeout were set in the Inventory system API or database, it would not help the Order API directly, as the OrderItems process API would still be waiting for a response.

Detailed Analysis of Each Option:

Option A (Correct Answer): Setting the timeout in the OrderItems process API allows it to control how long it waits for a response from the Inventory system API. If the Inventory system API does not respond within two seconds, the OrderItems process API can terminate the call and return a timeout response to the Order API, meeting the requirement.

Option B: Setting the timeout in the Order API would not limit the wait time at the OrderItems process API level, meaning the OrderItems process API could still wait indefinitely for the Inventory system API, leading to a longer delay.

Option C: Setting the timeout in the Inventory system API only affects the connection to the database and does not influence how long the OrderItems process API waits for the Inventory system API's response.

Option D: Setting a timeout in the database is not feasible in this context since database timeouts are typically configured for database operations and would not directly control the API response times in the overall API chain.

Conclusion:

Option A is the best choice, as it ensures that the OrderItems process API does not hold the Order API longer than the required two seconds, even if the downstream connection to the database hangs. This configuration aligns with MuleSoft best practices for setting timeouts in API orchestration to manage dependencies and prevent delays across a chain of API calls.

For additional information on timeout settings, refer to MuleSoft documentation on handling timeouts and API orchestration best practices.

Center for Enablement (C4E) KPIs:

A Center for Enablement (C4E) in MuleSoft focuses on enabling self-service and reuse by providing APIs that can be consumed across the organization. A key metric of success is how many consumers are utilizing the published APIs.

The number of consumers who have requested and received access to an API indicates the level of adoption and reuse, which aligns with the goals of a C4E.

Evaluating the Options:

Option A: This metric could indicate deployment automation, but it is not a direct measure of C4E’s success in enabling API reuse and consumption.

Option B: Bandwidth usage per API implementation provides insight into API traffic but does not measure C4E enablement or consumer engagement.

Option C: The number of developers downloading an API specification can be an indicator of interest but does not confirm actual usage or enablement.

Option D (Correct Answer): The number of consumers who have requested and received access to an API in production is a key metric indicating API adoption and reuse, which aligns with C4E’s goals.

Conclusion:

Option D is the correct answer as it provides a direct measure of consumer engagement and adoption, indicating the success of the C4E in promoting API usage across the organization.

Refer to MuleSoft’s documentation on C4E KPIs and API usage metrics for additional insights.