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

Topic
Details

Topic 1

Designing Integration Solutions to Meet Persistence Requirements: It addresses the usage of VM queues and connectors, object stores and services, and stateful components configured with object stores.

Topic 2

Designing Architecture Using Integration Paradigms: This topic focuses on creating high-level integration architectures using various paradigms. It includes API-led connectivity, web APIs and HTTP, event-driven APIs, and message brokers, and designing Mule application using messaging patterns and technologies.

Topic 3

Designing Integration Solutions to Meet Reliability Requirements: It includes selecting alternatives to traditional transactions, recognizing the purpose of various scopes and strategies, differentiating disaster recovery and high availability, and using local and XA transactions.

Topic 4

Initiating Integration Solutions on Anypoint Platform: Summarizing MuleSoft Catalyst and Catalyst Knowledge Hub, differentiating between functional and non-functional requirements, selecting features for designing and managing APIs, and choosing deployment options are its sub-topics.

Topic 5

Designing Automated Tests for Mule Applications: This topic covers unit test suites, and scenarios for integration and performance testing.

Salesforce Certified MuleSoft Integration Architect I Sample Questions (Q171-Q176):

NEW QUESTION # 171
Refer to the exhibit.

A business process involves the receipt of a file from an external vendor over SFTP. The file needs to be parsed and its content processed, validated, and ultimately persisted to a database. The delivery mechanism is expected to change in the future as more vendors send similar files using other mechanisms such as file transfer or HTTP POST.
What is the most effective way to design for these requirements in order to minimize the impact of future change?

A. Create a Process API to receive the file and process it using a MuleSoft Batch Job while delegating the data save process to a System API
B. Create an API that receives the file and invokes a Process API with the data contained In the file, then have the Process API process the data using a MuleSoft Batch Job and other System APIs as needed
C. Use a composite data source so files can be retrieved from various sources and delivered to a MuleSoft Batch Job for processing
D. Use a MuleSoft Scatter-Gather and a MuleSoft Batch Job to handle the different files coming from different sources

Answer: B

Explanation:
* Scatter-Gather is used for parallel processing, to improve performance. In this scenario, input files are coming from different vendors so mostly at different times. Goal here is to minimize the impact of future change. So scatter Gather is not the correct choice.
* If we use 1 API to receive all files from different Vendors, any new vendor addition will need changes to that 1 API to accommodate new requirements. So Option A and C are also ruled out.
* Correct answer is Create an API that receives the file and invokes a Process API with the data contained in the file, then have the Process API process the data using a MuleSoft Batch Job and other System APIs as needed. Answer to this question lies in the API led connectivity approach.
* API-led connectivity is a methodical way to connect data to applications through a series of reusable and purposeful modern APIs that are each developed to play a specific role - unlock data from systems, compose data into processes, or deliver an experience. System API : System API tier, which provides consistent, managed, and secure access to backend systems. Process APIs : Process APIs take core assets and combines them with some business logic to create a higher level of value. Experience APIs : These are designed specifically for consumption by a specific end-user app or device.
So in case of any future plans , organization can only add experience API on addition of new Vendors, which reuse the already existing process API. It will keep impact minimal.

NEW QUESTION # 172
A mule application must periodically process a large dataset which varies from 6 GB lo 8 GB from a back- end database and write transform data lo an FTPS server using a properly configured bad job scope.
The performance requirements of an application are approved to run in the cloud hub 0.2 vCore with 8 GB storage capacity and currency requirements are met.
How can the high rate of records be effectively managed in this application?

A. Use streaming with a file storage repeatable strategy for reading records from the database and batch aggregator with streaming to write to FTPS
B. Use streaming with a file store repeatable strategy reading records from the database and batch aggregator without any required configuration
C. Use streaming with an in-memory reputable store strategy for reading records from the database and batch aggregator with streaming to write to FTPS
D. Use streaming with a file store repeatable strategy for reading records from the database and batch aggregator with an optimal size

Answer: A

Explanation:
For handling large datasets in a Mule application, streaming is an effective strategy. Streaming allows the application to process large amounts of data in chunks, reducing memory usage and improving performance.
Using a file storage repeatable strategy for reading records from the database ensures that the data is read in manageable chunks and stored temporarily in files, which can be re-read if necessary, enhancing reliability.
The batch aggregator with streaming to write to an FTPS server ensures that data is written in chunks, aligning with the processing capabilities of the application running in CloudHub with 0.2 vCore and 8 GB storage. This configuration optimizes performance by balancing the load and managing the dataset size effectively, ensuring that the high rate of records can be processed and written to the FTPS server without overwhelming the system.
References:
* MuleSoft Documentation on Streaming
* MuleSoft Documentation on Batch Processing

NEW QUESTION # 173
According to MuleSoft, which deployment characteristic applies to a microservices application architecture?

A. Core business capabilities are encapsulated in a single, deployable application
B. A deployment to enhance one capability requires a redeployment of all capabilities
C. Services exist as independent deployment artifacts and can be scaled -independently of other services
D. All services of an application can be deployed together as single Java WAR file

Answer: C

Explanation:
In a microservices application architecture, each service is designed to be an independent deployment artifact.
This means that services can be deployed, updated, and scaled independently of one another. This characteristic allows for greater flexibility and agility in managing applications, as individual services can be scaled up or down based on demand without impacting other services. It also enhances fault isolation, as issues in one service do not necessarily affect the entire application.
This is in contrast to monolithic architectures, where all components are packaged and deployed together, often resulting in a single point of failure and difficulties in scaling and updating specific parts of the application.
References
* MuleSoft Documentation on Microservices Architecture
* Principles of Microservices Design

NEW QUESTION # 174
Organization wants to achieve high availability goal for Mule applications in customer hosted runtime plane.
Due to the complexity involved, data cannot be shared among of different instances of same Mule application.
What option best suits to this requirement considering high availability is very much critical to the organization?

A. Use third party product to implement load balancer
B. The cluster can be configured
C. Use persistent object store
D. High availability can be achieved only in CloudHub

Answer: A

Explanation:
High availability is about up-time of your application
A) High availability can be achieved only in CloudHub isn't correct statement. It can be achieved in customer hosted runtime planes as well B) An object store is a facility for storing objects in or across Mule applications. Mule runtime engine (Mule) uses object stores to persist data for eventual retrieval. It can be used for disaster recovery but not for High Availability. Using object store can't guarantee that all instances won't go down at once. So not an appropriate choice.

NEW QUESTION # 175
What is the MuleSoft-recommended best practice to share the connector and configuration information among the APIs?

A. Create an API proxy for each System API and share the Database connector configuration with all the API proxies via an automated policy
B. Build another System API that connects to the database, and refactor all the other APIs to make requests through the new System API to access the database
C. Build a Mule domain project, add the Database connector and configuration to it, and reference this one domain project from each System API
D. Build a separate Mule domain project for each API, and configure each of them to use a file on a shared file store to load the configuration information dynamically

Answer: C

Explanation:
The MuleSoft-recommended best practice for sharing the connector and configuration information among multiple APIs is to use a Mule domain project. The steps are:
* Create a Mule domain project.
* Add the Database connector and its configuration to the domain project.
* Reference this domain project from each System API that needs to use the Database connector and configuration.
By using a domain project, you centralize the configuration and reuse it across multiple APIs. This approach ensures consistency, reduces duplication, and simplifies maintenance and updates to the connector configuration.
References
* MuleSoft Documentation on Domain Projects
* Best Practices for Reusable Configuration in MuleSoft

NEW QUESTION # 176
......

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