MCPA-Level-1 MuleSoft Certified Platform Architect - Level 1 Questions and Answers

Explanation

Correct Answer: Create separate Anypoint VPCs for non-production and production environments, then configure connections to the backend systems in the corresponding customer-hosted environments.

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>> Creating different Business Groups does NOT make any difference w.r.t accessing the non-prod and prod customer-hosted environments. Still they will be accessing from both Business Groups unless process network restrictions are put in place.

>> We need to modify or couple the Mule Application Implementations with the environment. In fact, we should never implements application coupled with environments by binding them in the properties. Only basic things like endpoint URL etc should be bundled in properties but not environment level access restrictions.

>> IP addresses on CloudHub are dynamic until unless a special static addresses are assigned. So it is not possible to setup firewall rules in customer-hosted infrastrcture. More over, even if static IP addresses are assigned, there could be 100s of applications running on cloudhub and setting up rules for all of them would be a hectic task, non-maintainable and definitely got a good practice.

>> The best practice recommended by Mulesoft (In fact any cloud provider), is to have your Anypoint VPCs seperated for Prod and Non-Prod and perform the VPC peering or VPN tunneling for these Anypoint VPCs to respective Prod and Non-Prod customer-hosted environment networks.

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.

Explanation

Correct Answer: Drive consumption as much as production of assets; this enables developers to discover and reuse assets from other projects and encourages standardization

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>> The main motto of the new IT Operating Model that MuleSoft recommends and made popular is to change the way that they are delivered from a production model to a production + consumption model, which is done through an API strategy called API-led connectivity.

>> The assets built should also be discoverable and self-serveable for reusablity across LOBs and organization.

>> MuleSoft's IT operating model does not talk about SDLC model (Agile/ Lean etc) or MDM at all. So, options suggesting these are not valid.

References:

https://blogs.mulesoft.com/biz/connectivity/what-is-a-center-for-enablement-c4e/

https://www.mulesoft.com/resources/api/secret-to-managing-it-projects

Explanation

Correct Answer: A CloudHub worker fails with an out-of-memory exception.

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>> An AWS Region itself going down will definitely result in an outage as it does not matter how many workers are assigned to the Mule App as all of those in that region will go down. This is a complete downtime and outage.

>> Extended outage of API manager during initial deployment of API implementation will of course cause issues in proper application startup itself as the API Autodiscovery might fail or API policy templates and polices may not be downloaded to embed at the time of applicaiton startup etc... there are many reasons that could cause issues.

>> A network outage onpremises would of course cause the Order Management System not accessible and it does not matter how many workers are assigned to the app they all will fail and cause outage for sure.

The only option that does NOT result in a service outage is if a cloudhub worker fails with an out-of-memory exception. Even if a worker fails and goes down, there are still other workers to handle the requests and keep the API UP and Running. So, this is the right answer.

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: API-led connectivity is not just an architecture or technology but also a way to organize people and processes for efficient IT delivery in the organization.

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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.

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: 8082

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>> 8091 and 8092 ports are to be used when keeping your HTTP and HTTPS app private to the LOCAL VPC respectively.

>> Above TWO ports are not for Shared AWS VPC/ Shared Worker Cloud.

>> 8081 is to be used when exposing your HTTP endpoint app to the internet through Shared LB

>> 8082 is to be used when exposing your HTTPS endpoint app to the internet through Shared LB

So, API invocations should be sent to port 8082 when calling this HTTPS based app.

References:

https://docs.mulesoft.com/runtime-manager/cloudhub-networking-guide

https://help.mulesoft.com/s/article/Configure-Cloudhub-Application-to-Send-a-HTTPS-Request-Directly-to-Another-Cloudhub-Application

https://help.mulesoft.com/s/question/0D52T00004mXXULSA4/multiple-http-listerners-on-cloudhub-one-with-port-9090

Explanation

Correct Answer: When a pragmatic approach with only limited isolation from the backend system is deemed appropriate.

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General guidance w.r.t choosing Data Models:

>> If an Enterprise Data Model is in use then the API data model of System APIs should make use of data types from that Enterprise Data Model and the corresponding API implementation should translate between these data types from the Enterprise Data Model and the native data model of the backend system.

>> If no Enterprise Data Model is in use then each System API should be assigned to a Bounded Context, the API data model of System APIs should make use of data types from the corresponding Bounded Context Data Model and the corresponding API implementation should translate between these data types from the Bounded Context Data Model and the native data model of the backend system. In this scenario, the data types in the Bounded Context Data Model are defined purely in terms of their business characteristics and are typically not related to the native data model of the backend system. In other words, the translation effort may be significant.

>> If no Enterprise Data Model is in use, and the definition of a clean Bounded Context Data Model is considered too much effort, then the API data model of System APIs should make use of data types that approximately mirror those from the backend system, same semantics and naming as backend system, lightly sanitized, expose all fields needed for the given System API’s functionality, but not significantly more and making good use of REST conventions.

The latter approach, i.e., exposing in System APIs an API data model that basically mirrors that of the backend system, does not provide satisfactory isolation from backend systems through the System API tier on its own. In particular, it will typically not be possible to "swap out" a backend system without significantly changing all System APIs in front of that backend system and therefore the API implementations of all Process APIs that depend on those System APIs! This is so because it is not desirable to prolong the life of a previous backend system’s data model in the form of the API data model of System APIs that now front a new backend system. The API data models of System APIs following this approach must therefore change when the backend system is replaced.

On the other hand:

>> It is a very pragmatic approach that adds comparatively little overhead over accessing the backend system directly

>> Isolates API clients from intricacies of the backend system outside the data model (protocol, authentication, connection pooling, network address, …)

>> Allows the usual API policies to be applied to System APIs

>> Makes the API data model for interacting with the backend system explicit and visible, by exposing it in the RAML definitions of the System APIs

>> Further isolation from the backend system data model does occur in the API implementations of the Process API tier

Explanation

Correct Answer: The test runs immediately after the Mule application has been compiled and packaged

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>> Integration tests are the last layer of tests we need to add to be fully covered.

>> These tests actually run against Mule running with your full configuration in place and are tested from external source as they work in PROD.

>> These tests exercise the application as a whole with actual transports enabled. So, external systems are affected when these tests run.

So, these tests do NOT run immediately after the Mule application has been compiled and packaged.

FYI... Unit Tests are the one that run immediately after the Mule application has been compiled and packaged.