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Salesforce MuleSoft-Platform-Architect-I Exam Syllabus Topics:

Topic	Details
Topic 1	Governing Web APIs on Anypoint Platform: This topic includes sub-topics related to managing API instances and environments, selecting API policies, enforcing API policies, securing APIs, and understanding OAuth 2.0 relationships.
Topic 2	Meeting API Quality Goals: This topic focuses on designing resilience strategies, selecting appropriate caching and OS usage scenarios, and describing horizontal scaling benefits.
Topic 3	Deploying API Implementations to CloudHub: Understanding Object Store usage, selecting worker sizes, predicting app reliability and performance, and comparing load balancers. Avoiding single points of failure in deployments is also its sub-topic.
Topic 4	Designing and Sharing APIs: Identifying dependencies between API components, creating and publishing reusable API assets, mapping API data models between Bounded Contexts, and recognizing idempotent HTTP methods.
Topic 5	Monitoring and Analyzing Application Networks: It discusses Anypoint Platform components for data generation, collected metrics, and key alerts. This topic also includes specifying alerts to define Mule applications.
Topic 6	Designing APIs Using System, Process, and Experience Layers: Identifying suitable APIs for business processes, assigning them according to functional focus, and recommending data model approaches are its sub-topics.
Topic 7	Establishing Organizational and Platform Foundations: Advising on a Center for Enablement (C4E) and identifying KPIs, describing MuleSoft Catalyst's structure, comparing Identity and Client Management options, and identifying data residency types are essential sub-topics.

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Salesforce Certified MuleSoft Platform Architect I Sample Questions (Q140-Q145):

NEW QUESTION # 140
A company has started to create an application network and is now planning to implement a Center for Enablement (C4E) organizational model. What key factor would lead the company to decide upon a federated rather than a centralized C4E?

A. When there are a large number of existing common assets shared by development teams
B. When the majority of the applications in the application network are cloud based
C. When various teams responsible for creating APIs are new to integration and hence need extensive training
D. When development is already organized into several independent initiatives or groups

Answer: D

Explanation:
Correct Answer : When development is already organized into several independent initiatives or groups
*****************************************
>> It would require lot of process effort in an organization to have a single C4E team coordinating with multiple already organized development teams which are into several independent initiatives. A single C4E works well with different teams having at least a common initiative. So, in this scenario, federated C4E works well instead of centralized C4E.

NEW QUESTION # 141
Which APIs can be used with DataGraph to create a unified schema?

A. APIs 1, 2, s5, 6
B. APIs 1, 3, 5
C. APIs 2, 4 ,6
D. APIs 1, 2, 3, 4

Answer: D

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.
Reference
For more detailed information on setting up and managing unified schemas in DataGraph, refer to the DataWeave documentation and MuleSoft DataGraph resources which provide in-depth guidelines on schema aggregation and API federation.

NEW QUESTION # 142
An API implementation is being designed that must invoke an Order API, which is known to repeatedly experience downtime.
For this reason, a fallback API is to be called when the Order API is unavailable.
What approach to designing the invocation of the fallback API provides the best resilience?

A. Search Anypoint Exchange for a suitable existing fallback API, and then implement invocations to this fallback API in addition to the Order API
B. Redirect client requests through an HTTP 307 Temporary Redirect status code to the fallback API whenever the Order API is unavailable
C. Create a separate entry for the Order API in API Manager, and then invoke this API as a fallback API if the primary Order API is unavailable
D. Set an option in the HTTP Requester component that invokes the Order API to instead invoke a fallback API whenever an HTTP 4xx or 5xx response status code is returned from the Order API

Answer: A

Explanation:
Correct Answer : Search Anypoint exchange for a suitable existing fallback API, and then implement invocations to this fallback API in addition to the order API
*****************************************
>> It is not ideal and good approach, until unless there is a pre-approved agreement with the API clients that they will receive a HTTP 3xx temporary redirect status code and they have to implement fallback logic their side to call another API.
>> Creating separate entry of same Order API in API manager would just create an another instance of it on top of same API implementation. So, it does NO GOOD by using clone od same API as a fallback API. Fallback API should be ideally a different API implementation that is not same as primary one.
>> There is NO option currently provided by Anypoint HTTP Connector that allows us to invoke a fallback API when we receive certain HTTP status codes in response.
The only statement TRUE in the given options is to Search Anypoint exchange for a suitable existing fallback API, and then implement invocations to this fallback API in addition to the order API.

NEW QUESTION # 143
The Line of Business (LoB) of an eCommerce company is requesting a process that sends automated notifications via email every time a new order is processed through the customer's mobile application or through the internal company's web application. In the future, multiple notification channels may be added: for example, text messages and push notifications.
What is the most effective API-led connectivity approach for the scenario described above?

A. Create Experience APIs for both the web application and mobile application.
(Create 3 Process API to orchestrate and retrieve the email template from 2 databese.
Create a System API that sends the email using the Anypoint Connector for Email.
B. Create Experience APIs for both the web application and mobile application.
Create a Process API ta orchestrate, retrieve the email template from e database, and send the email using the Anypoint Connector for Email.
C. Create one Experience API for the web application and one for the mobile application, Create a Process API to orchestrate, retrieve the email template from a database, and send the email using the Anypoint Connector for Email.
D. Create one Experience API for the web application and one for the mobile application.
Create a Process API to orchestrate and retrieve the email template from = database.
Create a System API that sends the email using the Anypoint Connector for Email.
Create one Experience API for the web application and one for the mobile application.
Create a Process API to orchestrate and retrieve the email template from = database.
Create a System API that sends the email using the Anypoint Connector for Email.

Answer: D

Explanation:
In this scenario, the best approach to satisfy the API-led connectivity principles and support future scalability is:
Experience APIs:
Create separate Experience APIs for the web application and the mobile application. This allows each application to have an optimized interface, supporting different needs and potential differences in request/response structures or security configurations.
Process API:
A single Process API can be used to orchestrate the workflow, including retrieving the email template from a database and preparing the email content. By centralizing this logic in the Process layer, we can ensure it is reusable and easily adaptable for different notification channels in the future.
System API:
A System API specifically designed for sending emails (using the Anypoint Connector for Email) abstracts the email-sending functionality from the business logic. This approach ensures that the email-sending function is reusable and scalable, and it can easily be extended or modified if other notification channels (like SMS or push notifications) are added later.
Why Option A is Correct:
This structure aligns with API-led connectivity principles by separating concerns across Experience, Process, and System layers. It provides flexibility for future notification channels and isolates each layer's responsibility, making it easier to maintain and scale.
of Incorrect Options:
Option B lacks a separate System API for sending emails, which goes against the principle of isolating back-end functionality in System APIs.
Option C similarly lacks a dedicated System API, reducing flexibility and reusability.
Option D suggests creating multiple Process APIs for database retrieval, which adds unnecessary complexity and does not adhere to the single-orchestration principle typically followed in API-led design.
Reference
For further guidance on API-led connectivity and the responsibilities of each API layer, refer to MuleSoft's documentation on API-led architecture and design best practices.

NEW QUESTION # 144
A retail company with thousands of stores has an API to receive data about purchases and insert it into a single database. Each individual store sends a batch of purchase data to the API about every 30 minutes. The API implementation uses a database bulk insert command to submit all the purchase data to a database using a custom JDBC driver provided by a data analytics solution provider. The API implementation is deployed to a single CloudHub worker. The JDBC driver processes the data into a set of several temporary disk files on the CloudHub worker, and then the data is sent to an analytics engine using a proprietary protocol. This process usually takes less than a few minutes. Sometimes a request fails. In this case, the logs show a message from the JDBC driver indicating an out-of-file-space message. When the request is resubmitted, it is successful. What is the best way to try to resolve this throughput issue?

A. Increase the number of CloudHub workers
B. Increase the size of the CloudHub worker(s)
C. Use a CloudHub autoscaling policy to increase the size of the CloudHub worker
D. se a CloudHub autoscaling policy to add CloudHub workers

Answer: A

Explanation:
Correct Answer : Increase the size of the CloudHub worker(s)
*****************************************
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.

NEW QUESTION # 145
......

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