Salesforce MULESOFT-INTEGRATION-ARCHITECT-I Online Practice Questions and Exam Preparation

Salesforce MULESOFT-INTEGRATION-ARCHITECT-I Online Questions & Answers

• Question 1:

  A marketing organization is designing a Mule application to process campaign data. The Mule application will periodically check for a file in a SFTP location and process the records in the file. The size of the file can vary from 10MB to 5GB. Due to the limited availabiltty of vCores, the Mule application is deployed to a single CloudHub worker configured with vCore size 0.2.

  The application must transform and send different formats of this file to three different downstream SFTP locations.

  What is the most idiomatic (used for its intended purpose) and performant way to configure the SFTP operations or event sources to process the large files to support these deployment requirements?

  A. Use an in-memory repeatable stream
  B. Use a file-stored non-repeatable stream
  C. Use an in-memory non-repeatable stream
  D. Use a file-stored repeatable stream
  A. Use an in-memory repeatable stream

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  Explanation/Reference:

  Reference: https://docs.mulesoft.com/mule-runtime/4.4/streaming-about

• Question 2:

  Refer to the exhibit.

  

  This Mule application is deployed to multiple Cloudhub workers with persistent queue enabled. The retrievefile flow event source reads a CSV file from a remote SFTP server and then publishes each record in the CSV file to a VM queue. The processCustomerRecords flow's VM Listner receives messages from the same VM queue and then processes each message separately.

  How are messages routed to the cloudhub workers as messages are received by the VM Listener?

  A. Each message is routed to ONE of the Cloudhub workers in a DETERMINSTIC round robin fashion thereby EXACTLY BALANCING messages among the cloudhub workers
  B. Each messages routes to ONE of the available Clouhub workers in a NON-DETERMINSTIC non round-robin fashion thereby APPROXIMATELY BALANCING messages among the cloudhub workers
  C. Each message is routed to the SAME Cloudhub worker that retrieved the file, thereby BINDING ALL messages to ONLY that ONE Cloudhub worker
  D. Each message is duplicated to ALL of the Cloudhub workers, thereby SHARING EACH message with ALL the Cloudhub workers.
  B. Each messages routes to ONE of the available Clouhub workers in a NON-DETERMINSTIC non round-robin fashion thereby APPROXIMATELY BALANCING messages among the cloudhub workers

• Question 3:

  An integration Mule application is deployed to a customer-hosted multi-node Mule 4 runtime duster. The Mule application uses a Listener operation of a JMS connector to receive incoming messages from a JMS queue.

  How are the messages consumed by the Mule application?

  A. Depending on the JMS provider's configuration, either all messages are consumed by ONLY the primary cluster node or else ALL messages are consumed by ALL cluster nodes
  B. Regardless of the Listener operation configuration, all messages are consumed by ALL cluster nodes
  C. Depending on the Listener operation configuration, either all messages are consumed by ONLY the primary cluster node or else EACH message is consumed by ANY ONE cluster node
  D. Regardless of the Listener operation configuration, all messages are consumed by ONLY the primary cluster node
  C. Depending on the Listener operation configuration, either all messages are consumed by ONLY the primary cluster node or else EACH message is consumed by ANY ONE cluster node

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  Explanation/Reference:

  Correct answer is Depending on the Listener operation configuration, either all messages are consumed by ONLY the primary cluster node or else EACH message is consumed by ANY ONE cluster node For applications running in clusters,

  you have to keep in mind the concept of primary node and how the connector will behave. When running in a cluster, the JMS listener default behavior will be to receive messages only in the primary node, no matter what kind of destination

  you are consuming from. In case of consuming messages from a Queue, you'll want to change this configuration to receive messages in all the nodes of the cluster, not just the primary.

  This can be done with the primaryNodeOnly parameter:

• Question 4:

  According to MuleSoft, which major benefit does a Center for Enablement (C4E) provide for an enterprise and its lines of business?

  A. Enabling Edge security between the lines of business and public devices
  B. Centralizing project management across the lines of business
  C. Centrally managing return on investment (ROI) reporting from lines of business to leadership
  D. Accelerating self-service by the lines of business
  D. Accelerating self-service by the lines of business

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  Explanation/Reference:

  A Center for Enablement (C4E) provides significant benefits to an enterprise and its lines of business by accelerating self-service capabilities. A C4E is a cross-functional team that promotes the use of best practices, reusable assets, and

  consistent methodologies across the organization. By enabling lines of business to access and use shared resources, tools, and knowledge independently, a C4E empowers these units to deliver projects faster and more efficiently. This self-

  service model reduces reliance on central IT for every initiative, fosters innovation, and enhances the overall agility of the organization.

  References:

  MuleSoft Center for Enablement (C4E) Overview

  Best Practices for Implementing a C4E on Anypoint Platform A Center for Enablement (C4E) provides significant benefits by accelerating self-service capabilities for lines of business. Here's how:

  Self-Service Enablement: C4E empowers lines of business to access, use, and create integrations and APIs independently, without relying heavily on central IT teams. Guidelines and Standards: The C4E establishes best practices,

  standards, and reusable frameworks, ensuring that lines of business can develop solutions efficiently and consistently. Faster Time-to-Market: By enabling self-service, lines of business can rapidly develop and deploy solutions to meet their

  specific needs, resulting in faster time-to-market for new initiatives. Reduced Bottlenecks: Self-service reduces dependency on central IT, alleviating potential bottlenecks and allowing IT to focus on governance, security, and strategic

  projects.

  The C4E model thus accelerates innovation and responsiveness within the enterprise.

  References:

  Center for Enablement (C4E)

  Benefits of a C4E

• Question 5:

  A mule application is required to periodically process large data set from a back-end database to Salesforce CRM using batch job scope configured properly process the higher rate of records.

  The application is deployed to two cloudhub workers with no persistence queues enabled.

  What is the consequence if the worker crashes during records processing?

  A. Remaining records will be processed by a new replacement worker
  B. Remaining records be processed by second worker
  C. Remaining records will be left and processed
  D. All the records will be processed from scratch by the second worker leading to duplicate processing
  D. All the records will be processed from scratch by the second worker leading to duplicate processing

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  Explanation/Reference:

  When a Mule application uses batch job scope to process large datasets and is deployed on multiple CloudHub workers without persistence queues enabled, the following scenario occurs if a worker crashes:

  Batch Job Scope: Batch jobs are designed to handle large datasets by splitting the work into records and processing them in parallel. Non-Persistent Queues: When persistence is not enabled, the state of the batch processing is not stored

  persistently. This means that if a worker crashes, the state of the in-progress batch job is lost.

  Worker Crash Consequence:

  This behavior can cause issues such as duplicate data in Salesforce CRM and inefficiencies in processing.

  References:

  MuleSoft Batch Processing

  MuleSoft CloudHub Workers

• Question 6:

  An organization is evaluating using the CloudHub shared Load Balancer (SLB) vs creating a CloudHub dedicated load balancer (DLB). They are evaluating how this choice affects the various types of certificates used by CloudHub deplpoyed Mule applications, including MuleSoft-provided, customer-provided, or Mule application-provided certificates.

  What type of restrictions exist on the types of certificates that can be exposed by the CloudHub Shared Load Balancer (SLB) to external web clients over the public internet?

  A. Only MuleSoft-provided certificates are exposed.
  B. Only customer-provided wildcard certificates are exposed.
  C. Only customer-provided self-signed certificates are exposed.
  D. Only underlying Mule application certificates are exposed (pass-through)
  A. Only MuleSoft-provided certificates are exposed.

• Question 7:

  A manufacturing company is planning to deploy Mule applications to its own Azure Kubernetes Service infrastructure.

  The organization wants to make the Mule applications more available and robust by deploying each Mule application to an isolated Mule runtime in a Docker container while managing all the Mule applications from the MuleSoft-hosted control plane. What is the most idiomatic (used for its intended purpose) choice of runtime plane to meet these organizational requirements?

  A. Anypoint Platform Private Cloud Edition
  B. Anypoint Runtime Fabric
  C. CloudHub
  D. Anypoint Service Mesh
  B. Anypoint Runtime Fabric

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  Explanation/Reference:

  Reference: https://blogs.mulesoft.com/dev-guides/how-to-tutorials/anypoint-runtime-fabric/

• Question 8:

  A customer wants to use the mapped diagnostic context (MDC) and logging variables to enrich its logging and improve tracking by providing more context in the logs.

  The customer also wants to improve the throughput and lower the latency of message processing.

  As an Mulesoft integration architect can you advise, what should the customer implement to meet these requirements?

  A. Use synchronous logging and use pattern layout with [%MDC] in the log4j2.xml configuration file and then configure the logging variables
  B. Useasync logger at the level greater than INFO and use pattern layout with [%MDC] in the log4j2,xml configuration file and then configure the logging variables
  C. Useasync logger at the level equal to DEBUG orTRACEand use pattern layout with [%MDC] in the log4j2.xml configuration file and then configure the logging variables
  D. Use synchronous logging at the INFO DEBUG or Trace level and use pattern layout with [%MDC] in the log4j2.xml configuration file and then configure the logging variables
  B. Useasync logger at the level greater than INFO and use pattern layout with [%MDC] in the log4j2,xml configuration file and then configure the logging variables

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  Explanation/Reference:

  To meet the requirements of enriching logging with more context and improving throughput while reducing latency, the customer should use an asynchronous logging approach. Here's why option B is correct:

  Async Logger: Asynchronous logging helps in improving the performance by decoupling the logging from the main thread of execution. This reduces the latency of message processing since logging operations are offloaded to a separate

  thread.

  Logging Level Greater than INFO: Logging at levels such as WARN, ERROR, or FATAL ensures that only essential logs are written asynchronously, further enhancing performance. Avoiding DEBUG or TRACE levels unless necessary

  reduces the overhead of logging.

  Pattern Layout with [%MDC]: Using the pattern layout with [%MDC] in the log4j2.xml configuration file allows the inclusion of mapped diagnostic context in the logs. MDC is a feature that provides context-specific information, such as

  transaction IDs or user IDs, which helps in tracing and debugging.

  Example configuration in log4j2.xml:

  This configuration ensures that the logs are enriched with context information

  provided by MDC and processed asynchronously to maintain high throughput and low latency.

  References:

  Log4j2 Asynchronous Logging

  Mapped Diagnostic Context (MDC) in Log4j2

• Question 9:

  When the mule application using VM is deployed to a customer-hosted cluster or multiple cloudhub workers, how are messages consumed by the Mule engine?

  A. in non-deterministic way
  B. by starting an XA transaction for each new message
  C. in a deterministic way
  D. the primary only in order to avoid duplicate processing
  A. in non-deterministic way

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  Explanation/Reference:

  When a Mule application using VM (Virtual Machine) queues is deployed to a customer-hosted cluster or multiple CloudHub workers, the messages are consumed by the Mule engine in a non-deterministic way. Here's an in-depth

  explanation:

  VM Queues Overview:

  Deployment in Clusters or Multiple Workers:

  Non-Deterministic Message Consumption:

  Advantages:

  Considerations:

  References:

  MuleSoft Documentation on VM Connector

  MuleSoft Documentation on High Availability Clustering

  MuleSoft Documentation on Deploying to CloudHub

• Question 10:

  An organization is implementing a Quote of the Day API that caches today's quote. What scenario can use the CloudHub Object Store connector to persist the cache's state?

  A. When there is one deployment of the API implementation to CloudHub and another one to customer hosted mule runtime that must share the cache state.
  B. When there are two CloudHub deployments of the API implementation by two Anypoint Platform business groups to the same CloudHub region that must share the cache state.
  C. When there is one CloudHub deployment of the API implementation to three workers that must share the cache state.
  D. When there are three CloudHub deployments of the API implementation to three separate CloudHub regions that must share the cache state.
  C. When there is one CloudHub deployment of the API implementation to three workers that must share the cache state.

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  Explanation/Reference:

  Object Store Connector is a Mule component that allows for simple key-value storage. Although it can serve a wide variety of use cases, it is mainly design for: -Storing synchronization information, such as watermarks. -Storing temporal information such as access tokens. -Storing user information. Additionally, Mule Runtime uses Object Stores to support some of its own components, for example: -The Cache module uses an Object Store to maintain all of the cached data. The OAuth module (and every OAuth enabled connector) uses Object Stores to store the access and refresh tokens. Object Store data is in the same region as the worker where the app is initially deployed. For example, if you deploy to the Singapore region, the object store persists in the Singapore region. MuleSoft Reference : https://docs.mulesoft.com/object-store-connector/1.1/ Data can be shared between different instances of the Mule application. This is not recommended for Inter Mule app communication. Coming to the question, object store cannot be used to share cached data if it is deployed as separate Mule applications or deployed under separate Business Groups. Hence correct answer is When there is one CloudHub deployment of the API implementation to three workers that must share the cache state.