Question # 1
The AnyAirline organization's passenger reservations center is designing an integration solution that combines invocations of three different System APIs (bookFlight, bookHotel, and bookCar) in a business transaction. Each System API makes calls to a single database.
The entire business transaction must be rolled back when at least one of the APIs fails.
What is the most idiomatic (used for its intended purpose) way to integrate these APIs in near real-time that provides the best balance of consistency, performance, and reliability?
| A. Implement eXtended Architecture (XA) transactions between the API implementations Coordinate between the API implementations using a Saga pattern Implement caching in each API implementation to improve performance | B. Implement local transactions within each API implementation
Configure each API implementation to also participate in the same eXtended Architecture (XA) transaction
Implement caching in each API implementation to improve performance
| C. Implement local transactions in each API implementation Coordinate between the API implementations using a Saga pattern
Apply various compensating actions depending on where a failure occurs
| D. Implement an eXtended Architecture (XA) transaction manager in a Mule application using a Saga pattern
Connect each API implementation with the Mule application using XA transactions Apply various compensating actions depending on where a failure occurs
|
C. Implement local transactions in each API implementation Coordinate between the API implementations using a Saga pattern
Apply various compensating actions depending on where a failure occurs
Question # 2
An organization is designing an integration solution to replicate financial transaction data from a legacy system into a data warehouse (DWH).
The DWH must contain a daily snapshot of financial transactions, to be delivered as a CSV file. Daily transaction volume exceeds tens of millions of records, with significant spikes in volume during popular shopping periods.
What is the most appropriate integration style for an integration solution that meets the organization's current requirements?
| A. Event-driven architecture | B. Microservice architecture | C. API-led connectivity | D. Batch-triggered ETL |
D. Batch-triggered ETL
Explanation: Explanation
Correct answer is Batch-triggered ETL Within a Mule application, batch processing provides a construct for asynchronously processing larger-than-memory data sets that are split into individual records. Batch jobs allow for the description of a reliable process that automatically splits up source data and stores it into persistent queues, which makes it possible to process large data sets while providing reliability. In the event that the application is redeployed or Mule crashes, the job execution is able to resume at the point it stopped.
Question # 3
An organization is successfully using API led connectivity, however, as the application network grows, all the manually performed tasks to publish share and discover, register, apply policies to, and deploy an API are becoming repetitive pictures driving the organization to automate this process using efficient CI/'CD pipeline. Considering Anypoint platforms capabilities how should the organization approach automating is API lifecycle? | A. Use runtime manager rest apis for API management and mavenforAPI deployment | B. Use Maven with a custom configuration required for the API lifecycle | C. Use Anypoint CLI or Anypoint Platform REST apis with scripting language such as groovy | D. Use Exchange rest api's for API management and MavenforAPI deployment |
C. Use Anypoint CLI or Anypoint Platform REST apis with scripting language such as groovy
Explanation:
To automate the API lifecycle in a CI/CD pipeline efficiently, leveraging Anypoint Platform's capabilities is crucial. Anypoint CLI (Command Line Interface) and Anypoint Platform REST APIs provide robust tools for managing various aspects of the API lifecycle, such as publishing, sharing, discovering, registering, applying policies, and deploying APIs. By using these tools with a scripting language like Groovy, you can script and automate these tasks to reduce manual intervention, ensuring consistency and efficiency.
Anypoint CLI allows you to interact with the Anypoint Platform from the command line, enabling automated deployments, management of APIs, and configuration of policies. The Anypoint Platform REST APIs provide comprehensive programmatic access to the platform’s functionalities, allowing for seamless integration into CI/CD pipelines. By combining these with a scripting language, you can create scripts that automate repetitive tasks, streamline processes, and ensure that your API lifecycle management is both efficient and reliable.
References:
MuleSoft Documentation on Anypoint CLI
MuleSoft Documentation on Anypoint Platform REST APIs
Question # 4
How are the API implementation , API client, and API consumer combined to invoke and process an API ? | A. The API consumer creates an API implementation , which receives API invocations from an API such that they are processed for an API client | B. The API consumer creates an API client which sends API invocations to an API such that they are processed by an API implementation | C. An API client creates an API consumer, which receives API invocation from an API such that they are processed for an API implementation | D. The API client creates an API consumer which sends API invocations to an API such that they are processed by API implementation |
C. An API client creates an API consumer, which receives API invocation from an API such that they are processed for an API implementation
Explanation
The API consumer creates an API client which sends API invocations to an API such that they are processed by an API implementation
This is based on below definitions API client • An application component • that accesses a service • by invoking an API of that service - by definition of the term API over HTTP API consumer • A business role, which is often assigned to an individual • that develops API clients, i.e., performs the activities necessary for enabling an API client to invoke APIs API implementation • An application component • that implements the functionality
Question # 5
A company is building an application network and has deployed four Mule APIs: one experience API, one process API, and two system APIs. The logs from all the APIs are aggregated in an external log aggregation tool. The company wants to trace messages that are exchanged between multiple API implementations. What is the most idiomatic (based on its intended use) identifier that should be used to implement Mule event tracing across the multiple API implementations? | A. Mule event ID | B. Mule correlation ID | C. Client's IP address | D. DataWeave UUID |
B. Mule correlation ID
Explanation: Explanation
Correct answer is Mule correlation ID By design, Correlation Ids cannot be changed within a flow in Mule 4 applications and can be set only at source. This ID is part of the Event Context and is generated as soon as the message is received by the application. When a HTTP Request is received, the request is inspected for "X-Correlation-Id" header. If "X- Correlation-Id" header is present, HTTP connector uses this as the Correlation Id. If "X- Correlation-Id" header is NOT present, a Correlation Id is randomly generated. For Incoming HTTP Requests: In order to set a custom Correlation Id, the client invoking the HTTP request must set "X-Correlation-Id" header. This will ensure that the Mule Flow uses this Correlation Id. For Outgoing HTTP Requests: You can also propagate the existing Correlation Id to downstream APIs. By default, all outgoing HTTP Requests send "X-
Correlation-Id" header. However, you can choose to set a different value to "X-Correlation- Id" header or set "Send Correlation Id" to NEVER.
Question # 6
A mule application designed to fulfil two requirements
a) Processing files are synchronously from an FTPS server to a back-end database using VM intermediary queues for load balancing VM events
b) Processing a medium rate of records from a source to a target system using batch job scope
Considering the processing reliability requirements for FTPS files, how should VM queues be configured for processing files as well as for the batch job scope if the application is deployed to Cloudhub workers? | A. Use Cloud hub persistent queues for FTPS files processing
There is no need to configure VM queues for the batch jobs scope as it uses by default the worker's disc for VM queueing | B. Use Cloud hub persistent VM queue for FTPS file processing
There is no need to configure VM queues for the batch jobs scope as it uses by default the worker's JVM memory for VM queueing | C. Use Cloud hub persistent VM queues for FTPS file processing
Disable VM queue for the batch job scope | D. Use VM connector persistent queues for FTPS file processing Disable VM queue for the batch job scope |
A. Use Cloud hub persistent queues for FTPS files processing
There is no need to configure VM queues for the batch jobs scope as it uses by default the worker's disc for VM queueing
Explanation:
When processing files synchronously from an FTPS server to a back-end database using VM intermediary queues for load balancing VM events on CloudHub, reliability is critical. CloudHub persistent queues should be used for FTPS file processing to ensure that no data is lost in case of worker failure or restarts. These queues provide durability and reliability since they store messages persistently.
For the batch job scope, it is not necessary to configure additional VM queues. By default, batch jobs on CloudHub use the worker's disk for VM queueing, which is reliable for handling medium-rate records processing from a source to a target system. This approach ensures that both FTPS file processing and batch job processing meet reliability requirements without additional configuration for batch job scope.
References
MuleSoft Documentation on CloudHub and VM Queues
Anypoint Platform Best Practices
Question # 7
Mule application A receives a request Anypoint MQ message REQU with a payload containing a variable-length list of request objects. Application A uses the For Each scope to split the list into individual objects and sends each object as a message to an Anypoint MQ queue.
Service S listens on that queue, processes each message independently of all other messages, and sends a response message to a response queue.
Application A listens on that response queue and must in turn create and publish a response Anypoint MQ message RESP with a payload containing the list of responses sent by service S in the same order as the request objects originally sent in REQU.
Assume successful response messages are returned by service S for all request messages.
What is required so that application A can ensure that the length and order of the list of objects in RESP and REQU match, while at the same time maximizing message throughput?
| A. Use a Scatter-Gather within the For Each scope to ensure response message order Configure the Scatter-Gather with a persistent object store | B. Perform all communication involving service S synchronously from within the For Each scope, so objects in RESP are in the exact same order as request objects in REQU | C. Use an Async scope within the For Each scope and collect response messages in a second For Each scope in the order In which they arrive, then send RESP using this list of responses | D. Keep track of the list length and all object indices in REQU, both in the For Each scope and in all communication involving service Use persistent storage when creating RESP |
D. Keep track of the list length and all object indices in REQU, both in the For Each scope and in all communication involving service Use persistent storage when creating RESP
Explanation
Correct answer is Perform all communication involving service S synchronously from within the For Each scope, so objects in RESP are in the exact same order as request objects in REQU Explanation : Using Anypoint MQ, you can create two types of queues: Standard queue These queues don’t guarantee a specific message order. Standard queues are the best fit for applications in which messages must be delivered quickly. FIFO (first in, first out) queue These queues ensure that your messages arrive in order. FIFO queues are the best fit for applications requiring strict message ordering and exactly-once delivery, but in which message delivery speed is of less importance Use of FIFO queue is no where in the option and also it decreased throughput. Similarly persistent object store is not the preferred solution approach when you maximizing message throughput. This rules out one of the options. Scatter Gather does not support ObjectStore. This rules out one of the options. Standard Anypoint MQ queues don’t guarantee a specific message order hence using another for each block to collect response wont work as requirement here is to ensure the order. Hence considering all the above factors the feasible approach is Perform all communication involving service S synchronously from within the For Each scope, so objects in RESP are in the exact same order as request objects in REQU
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