Mulesoft MCPA-Level-1 Exam Questions

Explanation:
To create a unified schema in MuleSoft's DataGraph, APIs must be exposed in a way that allows DataGraph to pull and consolidate data from these APIs into a single schema accessible to consumers. DataGraph provides a federated approach, combining multiple APIs to form a single, unified API endpoint.
In this setup:
APIs 1, 2, 3, and 4 are suitable candidates for DataGraph because they are hosted within the Customer VPC on CloudHub and are accessible either through a Shared Load Balancer (LB) or a Dedicated Load Balancer (DLB). Both of these load balancers provide public access, which is a necessary condition for DataGraph as it must access the APIs to aggregate data.
APIs 5 and 6 are hosted on Customer Hosted Server 2, which is explicitly marked as "Not public". Since DataGraph requires API access through a publicly reachable endpoint to aggregate them into a unified schema, APIs 5 and 6 cannot be used with DataGraph in this configuration.
APIs 3 and 4 on Customer Hosted Server 1 appear accessible through a Shared LB, implying public accessibility that meets DataGraph’s requirements.
By combining APIs 1, 2, 3, and 4 within DataGraph, you can create a unified schema that enables clients to query data seamlessly from all these APIs as if it were from a single source.
This setup allows for efficient data retrieval and can simplify API consumption by reducing the need to call multiple APIs individually, thus optimizing performance and developer experience.

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

Explanation: 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.
Reference: https://docs.mulesoft.com/mule-runtime/3.9/testing-strategies#integrationtesting

Explanation: Explanation
Correct Answer: In parallel, invoke the system API deployed to the primary environment
and the system API deployed to the DR environment, and ONLY use the first response.
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>> The API requirement in the given scenario is to respond in least possible time.
>> The option that is suggesting to first try the API in primary environment and then
fallback to API in DR environment would result in successful response but NOT in least
possible time. So, this is NOT a right choice of implementation for given requirement.
>> Another option that is suggesting to ONLY invoke API in primary environment and to
add timeout and retries may also result in successful response upon retries but NOT in
least possible time. So, this is also NOT a right choice of implementation for given
requirement.
>> One more option that is suggesting to invoke API in primary environment and API in DR
environment in parallel using Scatter-Gather would result in wrong API response as it
would return merged results and moreover, Scatter-Gather does things in parallel which is
true but still completes its scope only on finishing all routes inside it. So again, NOT a right
choice of implementation for given requirement
The Correct choice is to invoke the API in primary environment and the API in DR
environment parallelly, and using ONLY the first response received from one of them

Create a bounded-context model for the system layer to closely match the backend data
model, and add an anti-corruption layer to let the different bounded contexts cooperate
across the system and process layers

Explanation: Explanation
Correct Answer: Create a bounded-context model for the system layer to closely match the
backend data model, and add an anti-corruption layer to let the different bounded contexts
cooperate across the system and process layers
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>> Canonical models are not an option here as the organization has already put in efforts
and created bounded-context models for Experience and Process APIs.
>> Anti-corruption layers for ALL APIs is unnecessary and invalid because it is mentioned
that experience and process APIs share same bounded-context model. It is just the System
layer APIs that need to choose their approach now.
>> So, having an anti-corruption layer just between the process and system layers will work
well. Also to speed up the approach, system APIs can mimic the backend system data
model.

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

Explanation:
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.

A CloudHub worker fails with an out-of-memory exception

Explanation: 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.