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MuleSoft MCIA-LEVEL-1 Exam Prep
MuleSoft Certified Integration Architect - Level 1 (Page 22 )

Updated On: 15-Sep-2026
Page 10 of 50
PDF with 244 Questions

A Mule application is being designed to do the following:
Step 1: Read a SalesOrder message from a JMS queue, where each SalesOrder consists of a header and a list of SalesOrderLineltems.
Step 2: Insert the SalesOrder header and each SalesOrderLineltem into different tables in an RDBMS. Step 3: Insert the SalesOrder header and the sum of the prices of all its SalesOrderLineltems into a table in a different RDBMS.
No SalesOrder message can be lost and the consistency of all SalesOrder-related information in both RDBMSs must be ensured at all times.

What design choice (including choice of transactions) and order of steps addresses these requirements?


  1. 1) Read the JMS message (NOT in an XA transaction)
    2) Perform BOTH DB inserts in ONE DB transaction
    3) Acknowledge the JMS message

  2. 1) Read the JMS message (NOT in an XA transaction)
    2) Perform EACH DB insert in a SEPARATE DB transaction
    3) Acknowledge the JMS message

  3. 1) Read the JMS message in an XA transaction
    2) In the SAME XA transaction, perform BOTH DB inserts but do NOT acknowledge the JMS message

  4. 1) Read and acknowledge the JMS message (NOT in an XA transaction)
    2) In a NEW XA transaction, perform BOTH DB inserts

Answer(s): A

Explanation:

● Option A says "Perform EACH DB insert in a SEPARATE DB transaction". In this case if first DB insert is successful and second one fails then first insert won't be rolled back causing inconsistency. This option is ruled out.
● Option D says Perform BOTH DB inserts in ONE DB transaction.
Rule of thumb is when one or more DB connections are required we must use XA transaction as local transactions support only one resource. So this option is also ruled out.
● Option B acknowledges the before DB processing, so message is removed from the queue. In case of system failure at later point, message can't be retrieved.
● Option C is Valid: Though it says "do not ack JMS message", message will be auto acknowledged at the end of transaction. Here is how we can ensure all components are part of XA transaction: https://docs.mulesoft.com/jms-connector/1.7/jms-transactions
Additional Information about transactions:
● XA Transactions - You can use an XA transaction to group together a series of operations from multiple transactional resources, such as JMS, VM or JDBC resources, into a single, very reliable, global transaction.
● The XA (eXtended Architecture) standard is an X/Open group standard which specifies the interface between a global transaction manager and local transactional resource managers.
The XA protocol defines a 2-phase commit protocol which can be used to more reliably coordinate and sequence a series of "all or nothing" operations across multiple servers, even servers of different types
● Use JMS ack if
– Acknowledgment should occur eventually, perhaps asynchronously
– The performance of the message receipt is paramount
– The message processing is idempotent
– For the choreography portion of the SAGA pattern
● Use JMS transactions
– For all other times in the integration you want to perform an atomic unit of work
– When the unit of work comprises more than the receipt of a single message
– To simply and unify the programming model (begin/commit/rollback)



What metrics about API invocations are available for visualization in custom charts using Anypoint Analytics?

  1. Request size, request HTTP verbs, response time
  2. Request size, number of requests, JDBC Select operation result set size
  3. Request size, number of requests, response size, response time
  4. Request size, number of requests, JDBC Select operation response time

Answer(s): C

Explanation:

Correct answer is Request size, number of requests, response size, response time Analytics API Analytics can provide insight into how your APIs are being used and how they are performing. From API Manager, you can access the Analytics dashboard, create a custom dashboard, create and manage charts, and create reports. From API Manager, you can get following types of analytics: - API viewing analytics - API events analytics - Charted metrics in API Manager
It can be accessed using: http://anypoint.mulesoft.com/analytics
API Analytics provides a summary in chart form of requests, top apps, and latency for a particular duration.
The custom dashboard in Anypoint Analytics contains a set of charts for a single API or for all APIs Each chart displays various API characteristics
– Requests size: Line chart representing size of requests in KBs
– Requests : Line chart representing number of requests over a period
– Response size : Line chart representing size of response in KBs
– Response time :Line chart representing response time in ms
* To check this, You can go to API Manager > Analytics > Custom Dashboard > Edit Dashboard > Create Chart > Metric


Reference:

https://docs.mulesoft.com/monitoring/api-analytics-dashboard
Additional Information:
The default dashboard contains a set of charts
– Requests by date: Line chart representing number of requests
– Requests by location: Map chart showing the number of requests for each country of origin
– Requests by application: Bar chart showing the number of requests from each of the top five registered applications
– Requests by platform: Ring chart showing the number of requests broken down by platform



What aspects of a CI/CD pipeline for Mute applications can be automated using MuleSoft-provided Maven plugins?

  1. Compile, package, unit test, deploy, create associated API instances in API Manager
  2. Import from API designer, compile, package, unit test, deploy, publish to Am/point Exchange
  3. Compile, package, unit test, validate unit test coverage, deploy
  4. Compile, package, unit test, deploy, integration test

Answer(s): C



A Mule application currently writes to two separate SQL Server database instances across the internet using a single XA transaction. It is 58. proposed to split this one transaction into two separate non-XA transactions with no other changes to the Mule application.
What non-functional requirement can be expected to be negatively affected when implementing this change?

  1. Throughput
  2. Consistency
  3. Response time
  4. Availability

Answer(s): B

Explanation:

Correct answer is Consistency as XA transactions are implemented to achieve this. XA transactions are added in the implementation to achieve goal of ACID properties. In the context of transaction processing, the acronym ACID refers to the four key properties of a transaction: atomicity, consistency, isolation, and durability. Atomicity : All changes to data are performed as if they are a single operation. That is, all the changes are performed, or none of them are. For example, in an application that transfers funds from one account to another, the atomicity property ensures that, if a debit is made successfully from one account, the corresponding credit is made to the other account. Consistency : Data is in a consistent state when a transaction starts and when it ends.For example, in an application that transfers funds from one account to another, the consistency property ensures that the total value of funds in both the accounts is the same at the start and end of each transaction. Isolation : The intermediate state of a transaction is invisible to other transactions. As a result, transactions that run concurrently appear to be serialized. For example, in an application that transfers funds from one account to another, the isolation property ensures that another transaction sees the transferred funds in one account or the other, but not in both, nor in neither. Durability : After a transaction successfully completes, changes to data persist and are not undone, even in the event of a system failure. For example, in an application that transfers funds from one account to another, the durability property ensures that the changes made to each account will not be reversed. MuleSoft reference: https://docs.mulesoft.com/mule-runtime/4.3/xa-transactions



A Mule application contains a Batch Job with two Batch Steps (Batch_Step_l and Batch_Step_2). A payload with 1000 records is received by the Batch Job.
How many threads are used by the Batch Job to process records, and how does each Batch Step process records within the Batch Job?

  1. Each Batch Job uses SEVERAL THREADS for the Batch Steps Each Batch Step instance receives ONE record at a time as the payload, and RECORDS are processed IN PARALLEL within and between the two Batch Steps
  2. Each Batch Job uses a SINGLE THREAD for all Batch steps Each Batch step instance receives ONE record at a time as the payload, and RECORDS are processed IN ORDER, first through Batch_Step_l and then through Batch_Step_2
  3. Each Batch Job uses a SINGLE THREAD to process a configured block size of record Each Batch Step instance receives A BLOCK OF records as the payload, and BLOCKS of records are processed IN ORDER
  4. Each Batch Job uses SEVERAL THREADS for the Batch Steps Each Batch Step instance receives ONE record at a time as the payload, and BATCH STEP INSTANCES execute IN PARALLEL to process records and Batch Steps in ANY order as fast as possible

Answer(s): A

Explanation:

* Each Batch Job uses SEVERAL THREADS for the Batch Steps
* Each Batch Step instance receives ONE record at a time as the payload. It's not received in a block, as it does not wait for multiple records to be completed before moving to next batch step. (So Option D is out of choice)
* RECORDS are processed IN PARALLEL within and between the two Batch Steps.
* RECORDS are not processed in order. Let's say if second record completes batch_step_1 before record 1, then it moves to batch_step_2 before record 1. (So option C and D are out of choice)
* A batch job is the scope element in an application in which Mule processes a message payload as a batch of records. The term batch job is inclusive of all three phases of processing: Load and Dispatch, Process, and On Complete.
* A batch job instance is an occurrence in a Mule application whenever a Mule flow executes a batch job. Mule creates the batch job instance in the Load and Dispatch phase. Every batch job instance is identified internally using a unique String known as batch job instance id.



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