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.

Explanation

Correct Answer: Use a combination of CloudHub-deployed and manually provisioned on-premises Mule runtimes managed by the MuleSoft-hosted Platform control plane.

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Key details to be taken from the given scenario:

> > Organization uses BOTH cloud-based and on-premises systems

> > On-premises systems can only be accessed from within the organization ' s intranet

Let us evaluate the given choices based on above key details:

> > CloudHub-deployed Mule runtimes can ONLY be controlled using MuleSoft-hosted control plane. We CANNOT use Private Cloud Edition ' s control plane to control CloudHub Mule Runtimes. So, option suggesting this is INVALID

> > Using CloudHub-deployed Mule runtimes in the shared worker cloud managed by the MuleSoft-hosted Anypoint Platform is completely IRRELEVANT to given scenario and silly choice. So, option suggesting this is INVALID

> > Using an on-premises installation of Mule runtimes that are completely isolated with NO external network access, managed by the Anypoint Platform Private Cloud Edition control plane would work for On-premises integrations. However, with NO external access, integrations cannot be done to SaaS-based apps. Moreover CloudHub-hosted apps are best-fit for integrating with SaaS-based applications. So, option suggesting this is BEST WAY.

The best way to configure and use Anypoint Platform to support these mixed/hybrid integrations is to use a combination of CloudHub-deployed and manually provisioned on-premises Mule runtimes managed by the MuleSoft-hosted Platform control plane.

Explanation

Correct Answer: Convince the development team of the product API to adopt the API data model of the Order API such that integration logic of the Order API can work with one consistent internal data model

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Key details to note from the given scenario:

> > Power relationship between Order API and Product API is customer/supplier

So, as per below rules of " Power Relationships " , the caller (in this case Order API) would request for features to the called (Product API team) and the Product API team would need to accomodate those requests.

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: Increase the size of the CloudHub worker(s)

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The key details that we can take out from the given scenario are:

> > API implementation uses a database bulk insert command to submit all the purchase data to a database

> > JDBC driver processes the data into a set of several temporary disk files on the CloudHub worker

> > Sometimes a request fails and the logs show a message indicating an out-of-file-space message

Based on above details:

> > Both auto-scaling options does NOT help because we cannot set auto-scaling rules based on error messages. Auto-scaling rules are kicked-off based on CPU/Memory usages and not due to some given error or disk space issues.

> > Increasing the number of CloudHub workers also does NOT help here because the reason for the failure is not due to performance aspects w.r.t CPU or Memory. It is due to disk-space.

> > Moreover, the API is doing bulk insert to submit the received batch data. Which means, all data is handled by ONE worker only at a time. So, the disk space issue should be tackled on " per worker " basis. Having multiple workers does not help as the batch may still fail on any worker when disk is out of space on that particular worker.

Therefore, the right way to deal this issue and resolve this is to increase the vCore size of the worker so that a new worker with more disk space will be provisioned.

Explanation

Correct Answer: They provide an additional layer of resilience on top of the underlying backend system, thereby insulating clients from extended failure of these systems.

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In API-led connectivity,

> > Experience APIs - allow for innovation at the user interface level by consuming the underlying assets without being aware of how data is being extracted from backend systems.

> > Process APIs - compose data from various sources and combine them with orchestration logic to create higher level value

> > System APIs - reduce the dependency on the underlying backend systems by helping unlock data from backend systems in a reusable and consumable way.

However, they NEVER promise that they provide an additional layer of resilience on top of the underlying backend system, thereby insulating clients from extended failure of these systems.

https://dzone.com/articles/api-led-connectivity-with-mule

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: A higher number of discoverable API-related assets in the application network.

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> > We do NOT get faster response times in fine-grained approach when compared to coarse-grained approach.

> > In fact, we get faster response times from a network having coarse-grained APIs compared to a network having fine-grained APIs model. The reasons are below.

Fine-grained approach:

1. will have more APIs compared to coarse-grained

2. So, more orchestration needs to be done to achieve a functionality in business process.

3. Which means, lots of API calls to be made. So, more connections will needs to be established. So, obviously more hops, more network i/o, more number of integration points compared to coarse-grained approach where fewer APIs with bulk functionality embedded in them.

4. That is why, because of all these extra hops and added latencies, fine-grained approach will have bit more response times compared to coarse-grained.

5. Not only added latencies and connections, there will be more resources used up in fine-grained approach due to more number of APIs.

That ' s why, fine-grained APIs are good in a way to expose more number of resuable assets in your network and make them discoverable. However, needs more maintenance, taking care of integration points, connections, resources with a little compromise w.r.t network hops and response times.

Explanation

Correct Answer: API Consumer

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> > API clients are piece of code or programs that use the client credentials of API consumer but does not directly interact with Anypoint Exchange to get the access

> > API consumer is the one who should get registered and request access to API and then API client needs to use those client credentials to hit the APIs

So, API consumer is the one who needs to request access on the API from Anypoint Exchange

Explanation

Correct Answer: Option A

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> > Anypoint API Manager and API policies are applicable to all types of HTTP/1.x APIs.

> > They are not applicable to WebSocket APIs, HTTP/2 APIs and gRPC APIs