An organization has created an API-led architecture that uses various API layers to integrate mobile clients with a backend system. The backend system consists of a number of specialized components and can be accessed via a REST API. The process and
experience APIs share the same bounded-context model that is different from the backend
data model. What additional canonical models, bounded-context models, or anti-corruption
layers are best added to this architecture to help process data consumed from the backend
system?
A.
Create a bounded-context model for every layer and overlap them when the boundary
contexts overlap, letting API developers know about the differences between upstream and
downstream data models
B.
Create a canonical model that combines the backend and API-led models to simplify
and unify data models, and minimize data transformations.
C.
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
D.
Create an anti-corruption layer for every API to perform transformation for every data
model to match each other, and let data simply travel between APIs to avoid the complexity
and overhead of building canonical models
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
*****************************************
>> 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.
Refer to the exhibit.
A.
Option A
B.
Option B
C.
Option C
D.
Option D
Option A
Explanation: Explanation
Correct Answer: Ask the Marketing Department to interact with a mocking implementation
of the API using the automatically generated API Console.
*****************************************
As per MuleSoft's IT Operating Model:
>> API consumers need NOT wait until the full API implementation is ready.
>> NO technical test-suites needs to be shared with end users to interact with APIs.
>> Anypoint Platform offers a mocking capability on all the published API specifications to
Anypoint Exchange which also will be rich in documentation covering all details of API
functionalities and working nature.
>> No needs of arranging days of workshops with end users for feedback.
API consumers can use Anypoint Exchange features on the platform and interact with the
API using its mocking feature. The feedback can be shared quickly on the same to
incorporate any changes.
When can CloudHub Object Store v2 be used?
A. To store an unlimited number of key-value pairs
B. To store payloads with an average size greater than 15MB
C. To store information in Mule 4 Object Store v1
D. To store key-value pairs with keys up to 300 characters
Explanation: CloudHub Object Store v2 is a managed key-value store provided by
MuleSoft to support various use cases where temporary data storage is required. Here’s
why Option D is correct:
Key Length Support: Object Store v2 allows storage of keys with a length of up to
300 characters, making it suitable for applications needing flexible and descriptive
keys.
Limitations on Size:
Key-Value Limits: Object Store v2 is designed for moderate, transient storage
needs, and does not support unlimited storage. Thus, Option A is incorrect.
Backward Compatibility: Object Store v2 does not support Mule 4 applications
running Object Store v1. Option C is incorrect as Object Store v1 and v2 are
distinct.
What is true about automating interactions with Anypoint Platform using tools such as Anypoint Platform REST APIs, Anypoint CU, or the Mule Maven plugin?
A.
Access to Anypoint Platform APIs and Anypoint CU can be controlled separately through the roles and permissions in Anypoint Platform, so that specific users can get access to Anypoint CLI white others get access to the platform APIs
B.
Anypoint Platform APIs can ONLY automate interactions with CloudHub, while the Mule Maven plugin is required for deployment to customer-hosted Mule runtimes
C.
By default, the Anypoint CLI and Mule Maven plugin are NOT included in the Mule runtime, so are NOT available to be used by deployed Mule applications
D.
API policies can be applied to the Anypoint Platform APIs so that ONLY certain LOBs have access to specific functions
By default, the Anypoint CLI and Mule Maven plugin are NOT included in the Mule runtime, so are NOT available to be used by deployed Mule applications
Explanation: Explanation
Correct Answer: By default, the Anypoint CLI and Mule Maven plugin are NOT included in
the Mule runtime, so are NOT available to be used by deployed Mule applications
*****************************************
>> We CANNOT apply API policies to the Anypoint Platform APIs like we can do on our
custom written API instances. So, option suggesting this is FALSE.
>> Anypoint Platform APIs can be used for automating interactions with both CloudHub
and customer-hosted Mule runtimes. Not JUST the CloudHub. So, option opposing this is
FALSE.
>> Mule Maven plugin is NOT mandatory for deployment to customer-hosted Mule
runtimes. It just helps your CI/CD to have smoother automation. But not a compulsory
requirement to deploy. So, option opposing this is FALSE.
>> We DO NOT have any such special roles and permissions on the platform to separately
control access for some users to have Anypoint CLI and others to have Anypoint Platform
APIs. With proper general roles/permissions (API Owner, Cloudhub Admin etc..), one can
use any of the options (Anypoint CLI or Platform APIs). So, option suggesting this is
FALSE.
Only TRUE statement given in the choices is that - Anypoint CLI and Mule Maven plugin
are NOT included in the Mule runtime, so are NOT available to be used by deployed Mule
applications.
Maven is part of Studio or you can use other Maven installation for development.
CLI is convenience only. It is one of many ways how to install app to the runtime.
These are definitely NOT part of anything except your process of deployment or
automation.
A customer has an ELA contract with MuleSoft. An API deployed to CloudHub is consistently experiencing performance issues. Based on the root cause analysis, it is determined that autoscaling needs to be applied. How can this be achieved?
A. Configure a policy so that when the number of HTTP requests reaches a certain threshold the number of workers/replicas increases (horizontal scaling)
B. Configure two separate policies: When CPU and memory reach certain threshold, increase the worker/replica type (vertical sealing) and the number of workers/replicas (horizontal sealing)
C. Configure a policy based on CPU usage so that CloudHub auto-adjusts the number of workers/replicas (horizontal scaling)
D. Configure a policy so that when the response time reaches a certain threshold the worker/replica type increases (vertical scaling)
Explanation:
In MuleSoft CloudHub, autoscaling is essential to managing application load
efficiently. CloudHub supports horizontal scaling based on CPU usage, which is wellsuited
to applications experiencing variable demand and needing responsive resource
allocation.
When designing an upstream API and its implementation, the development team has been
advised to NOT set timeouts when invoking a downstream API, because that downstream
API has no SLA that can be relied upon. This is the only downstream API dependency of
that upstream API.
Assume the downstream API runs uninterrupted without crashing. What is the impact of
this advice?
A.
An SLA for the upstream API CANNOT be provided
B.
The invocation of the downstream API will run to completion without timing out
C.
A default timeout of 500 ms will automatically be applied by the Mule runtime in which the upstream API implementation executes
D.
A toad-dependent timeout of less than 1000 ms will be applied by the Mule runtime in
which the downstream API implementation executes
An SLA for the upstream API CANNOT be provided
Explanation: Explanation
Correct Answer: An SLA for the upstream API CANNOT be provided.
*****************************************
>> First thing first, the default HTTP response timeout for HTTP connector is 10000 ms (10
seconds). NOT 500 ms.
>> Mule runtime does NOT apply any such "load-dependent" timeouts. There is no such
behavior currently in Mule.
>> As there is default 10000 ms time out for HTTP connector, we CANNOT always
guarantee that the invocation of the downstream API will run to completion without timing
out due to its unreliable SLA times. If the response time crosses 10 seconds then the
request may time out.
The main impact due to this is that a proper SLA for the upstream API CANNOT be
provided.
Reference: https://docs.mulesoft.com/http-connector/1.5/http-documentation#parameters-3
An application updates an inventory running only one process at any given time to keep the inventory consistent. This process takes 200 milliseconds (.2 seconds) to execute; therefore, the scalability threshold of the application is five requests per second. What is the impact on the application if horizontal scaling is applied, thereby increasing the number of Mule workers?
A. The application scalability threshold is five requests per second regardless of the horizontal scaling
B. The total process execution time is now 100 milliseconds (.1 seconds)
C. The application scalability threshold is now 10 requests per second
D. Horizontal scaling cannot be applied to an already-running application
Explanation:
Given that the application is designed to handle only one process at a time
to maintain data consistency, here’s why horizontal scaling won’t increase the
processing limit:
Single-Process Constraint:
What is the most performant out-of-the-box solution in Anypoint Platform to track
transaction state in an asynchronously executing long-running process implemented as a
Mule application deployed to multiple CloudHub workers?
A.
Redis distributed cache
B.
java.util.WeakHashMap
C.
Persistent Object Store
D.
File-based storage
Persistent Object Store
Explanation: Correct Answer: Persistent Object Store
*****************************************
>> Redis distributed cache is performant but NOT out-of-the-box solution in Anypoint
Platform
>> File-storage is neither performant nor out-of-the-box solution in Anypoint Platform
>> java.util.WeakHashMap needs a completely custom implementation of cache from
scratch using Java code and is limited to the JVM where it is running. Which means the
state in the cache is not worker aware when running on multiple workers. This type of
cache is local to the worker. So, this is neither out-of-the-box nor worker-aware among
multiple workers on cloudhub. https://www.baeldung.com/java-weakhashmap
>> Persistent Object Store is an out-of-the-box solution provided by Anypoint Platform
which is performant as well as worker aware among multiple workers running on
CloudHub. https://docs.mulesoft.com/object-store/
So, Persistent Object Store is the right answer.
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