MULESOFT-PLATFORM-ARCHITECT-I TEST QUESTIONS & MULESOFT-PLATFORM-ARCHITECT-I TEST DUMPS & MULESOFT-PLATFORM-ARCHITECT-I STUDY GUIDE

MuleSoft-Platform-Architect-I Test Questions & MuleSoft-Platform-Architect-I Test Dumps & MuleSoft-Platform-Architect-I Study Guide

MuleSoft-Platform-Architect-I Test Questions & MuleSoft-Platform-Architect-I Test Dumps & MuleSoft-Platform-Architect-I Study Guide

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

TopicDetails
Topic 1
  • Architecting and Deploying API Implementations: It covers important aspects like using auto-discovery, identifying VPC requirements, comparing hosting options and understanding testing methods. The topic also involves automated building, testing, and deploying in a DevOps setting.
Topic 2
  • 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 3
  • Explaining Application Network Basics: This topic includes sub-topics related to identifying and differentiating between technologies for API-led connectivity, describing the role and characteristics of web APIs, assigning APIs to tiers, and understanding Anypoint Platform components.
Topic 4
  • 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 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.

Salesforce Certified MuleSoft Platform Architect I Sample Questions (Q35-Q40):

NEW QUESTION # 35
An Order API triggers a sequence of other API calls to look up details of an order's items in a back-end inventory database. The Order API calls the OrderItems process API, which calls the Inventory system API. The Inventory system API performs database operations in the back-end inventory database.
The network connection between the Inventory system API and the database is known to be unreliable and hang at unpredictable times.
Where should a two-second timeout be configured in the API processing sequence so that the Order API never waits more than two seconds for a response from the Orderltems process API?

  • A. In the Inventory system API implementation
  • B. In the inventory database
  • C. In the Orderltems process API implementation
  • D. In the Order API implementation

Answer: C

Explanation:
Understanding the API Flow and Timeout Requirement:
The Order API initiates a call to the OrderItems process API, which in turn calls the Inventory system API to fetch details from the inventory database.
The requirement specifies that the Order API should not wait more than two seconds for a response from the OrderItems process API, even if there are delays further down the chain (between Inventory system API and the database).
Choosing the Appropriate Timeout Location:
Setting the timeout at the OrderItems process API level ensures that if the Inventory system API takes longer than two seconds to respond, the OrderItems process API will terminate the request and send a timeout response back to the Order API. This prevents the Order API from waiting indefinitely due to the unreliable connection to the database.
If the timeout were set in the Inventory system API or database, it would not help the Order API directly, as the OrderItems process API would still be waiting for a response.
Detailed Analysis of Each Option:
Option A (Correct Answer): Setting the timeout in the OrderItems process API allows it to control how long it waits for a response from the Inventory system API. If the Inventory system API does not respond within two seconds, the OrderItems process API can terminate the call and return a timeout response to the Order API, meeting the requirement.
Option B: Setting the timeout in the Order API would not limit the wait time at the OrderItems process API level, meaning the OrderItems process API could still wait indefinitely for the Inventory system API, leading to a longer delay.
Option C: Setting the timeout in the Inventory system API only affects the connection to the database and does not influence how long the OrderItems process API waits for the Inventory system API's response.
Option D: Setting a timeout in the database is not feasible in this context since database timeouts are typically configured for database operations and would not directly control the API response times in the overall API chain.
Conclusion:
Option A is the best choice, as it ensures that the OrderItems process API does not hold the Order API longer than the required two seconds, even if the downstream connection to the database hangs. This configuration aligns with MuleSoft best practices for setting timeouts in API orchestration to manage dependencies and prevent delays across a chain of API calls.
For additional information on timeout settings, refer to MuleSoft documentation on handling timeouts and API orchestration best practices.


NEW QUESTION # 36
A large organization with an experienced central IT department is getting started using MuleSoft. There is a project to connect a siloed back-end system to a new Customer Relationship Management (CRM) system. The Center for Enablement is coaching them to use API-led connectivity.
What action would support the creation of an application network using API-led connectivity?

  • A. Determine if the new CRM system supports the creation of custom: REST APIs, establishes 4 private network with CloudHub, and supports GAuth 2.0 authentication
  • B. Create a System API to unlock the data on the back-end system using a REST API
  • C. Invite the business analyst to create a business process model to specify the canonical data model between the two systems
  • D. To expedite this project, central IT should extend the CRM system and back-end systems to connect to one another using built in integration interfaces

Answer: B

Explanation:
For an organization starting with API-led connectivity to integrate a siloed back-end system with a new CRM, the following approach aligns with best practices and MuleSoft's Center for Enablement (C4E) guidance:
API-led Connectivity: This model organizes APIs into distinct layers (System, Process, and Experience) to improve reusability, modularity, and manageability.
System APIs are used to expose and unlock data from core systems (such as back-end applications or databases).
Process APIs orchestrate data across multiple systems and transform it as needed.
Experience APIs format the data specifically for consumption by applications or devices, such as the CRM in this case.
Step to Support Application Network:
Create a System API for the back-end system. This API should expose the necessary data to support integration with the CRM.
By creating a System API with a RESTful interface, data can be accessed in a standardized way, making it easier to integrate with other systems and supporting future scalability.
Why Option D is Correct:
Creating a System API aligns with the principle of API-led connectivity, ensuring that data is exposed in a reusable manner. This API can then be orchestrated by Process APIs as needed to meet CRM requirements and can easily be extended to other applications.
of Incorrect Options:
Option A (creating a business process model) does not directly enable connectivity or expose back-end data through APIs.
Option B is unnecessary at this stage; assessing CRM capabilities like OAuth 2.0 support is not directly related to creating the application network via System APIs.
Option C contradicts API-led best practices by suggesting a point-to-point integration, which API-led connectivity seeks to avoid due to its lack of flexibility and scalability.
Reference
Refer to MuleSoft's API-led Connectivity resources for a detailed framework on building scalable integration layers using System, Process, and Experience APIs.


NEW QUESTION # 37
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 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
  • B. 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
  • C. 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
  • D. Create a canonical model that combines the backend and API-led models to simplify and unify data models, and minimize data transformations.

Answer: A

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.


NEW QUESTION # 38
When must an API implementation be deployed to an Anypoint VPC?

  • A. When the API Implementation must write to a persistent Object Store
  • B. When the API implementation must be deployed to a production AWS VPC using the Mule Maven plugin
  • C. When the API Implementation must invoke publicly exposed services that are deployed outside of CloudHub in a customer- managed AWS instance
  • D. When the API implementation must be accessible within a subnet of a restricted customer-hosted network that does not allow public access

Answer: C


NEW QUESTION # 39
A code-centric API documentation environment should allow API consumers to investigate and execute API client source code that demonstrates invoking one or more APIs as part of representative scenarios.
What is the most effective way to provide this type of code-centric API documentation environment using Anypoint Platform?

  • A. Enable mocking services for each of the relevant APIs and expose them via their Anypoint Exchange entry
  • B. Ensure the APIs are well documented through their Anypoint Exchange entries and API Consoles and share these pages with all API consumers
  • C. Create API Notebooks and include them in the relevant Anypoint Exchange entries
  • D. Make relevant APIs discoverable via an Anypoint Exchange entry

Answer: C

Explanation:
Correct Answer : Create API Notebooks and Include them in the relevant Anypoint exchange entries
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>> API Notebooks are the one on Anypoint Platform that enable us to provide code-centric API documentation Reference:

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NEW QUESTION # 40
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