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Salesforce Certified MuleSoft Integration Architect I Sample Questions (Q139-Q144):

NEW QUESTION # 139
A marketing organization is designing a Mule application to process campaign dat a. 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 an in-memory non-repeatable stream
C. Use a file-stored repeatable stream
D. Use a file-stored non-repeatable stream

Answer: A

NEW QUESTION # 140
One of the backend systems involved by the API implementation enforces rate limits on the number of request a particle client can make.
Both the back-end system and API implementation are deployed to several non-production environments including the staging environment and to a particular production environment. Rate limiting of the back-end system applies to all non-production environments.
The production environment however does not have any rate limiting.
What is the cost-effective approach to conduct performance test of the API implementation in the non- production staging environment?

A. Including logic within the API implementation that bypasses in locations of the back-end system in the staging environment and invoke a Mocking service that replicates typical back-end system responses Then conduct performance test using this API implementation
B. Use MUnit to simulate standard responses from the back-end system.
Then conduct performance test to identify other bottlenecks in the system
C. Conduct scaled-down performance tests in the staging environment against rate-limiting back-end system. Then upscale performance results to full production scale
D. Create a Mocking service that replicates the back-end system's production performance characteristics Then configure the API implementation to use the mocking service and conduct the performance test

Answer: D

Explanation:
To conduct performance testing in a non-production environment where rate limits are enforced, the most cost-effective approach is:
C: Create a Mocking service that replicates the back-end system's production performance characteristics. Then configure the API implementation to use the mocking service and conduct the performance test.
* Mocking Service: Develop a mock service that emulates the performance characteristics of the production back-end system. This service should mimic the response times, data formats, and any relevant behavior of the actual back-end system without imposing rate limits.
* Configuration: Modify the API implementation to route requests to the mocking service instead of the actual back-end system. This ensures that the performance tests are not impacted by the rate limits imposed in the non-production environment.
* Performance Testing: Conduct the performance tests using the API implementation configured with the mocking service. This approach allows you to assess the performance under expected production load conditions without being constrained by non-production rate limits.
This method ensures that performance testing is accurate and reflective of the production environment without additional costs or constraints due to rate limiting in staging environments.
References:
* MuleSoft Documentation: Mocking Services
* MuleSoft Documentation: Performance Testing

NEW QUESTION # 141
According to MuleSoft's API development best practices, which type of API development approach starts with writing and approving an API contract?

A. Agile
B. Implement-first
C. Catalyst
D. Design-first

Answer: D

NEW QUESTION # 142
What aspects of a CI/CD pipeline for Mule applications can be automated using MuleSoft-provided Maven plugins?

A. Compile, package, unit test, deploy, create associated API instances in API Manager
B. Import from API designer, compile, package, unit test, deploy, publish to Anypoint Exchange
C. Compile, package, unit test, validate unit test coverage, deploy
D. Compile, package, unit test, deploy, integration test (Incorrect)

Answer: C

Explanation:
Correct answer is "Compile, package, unit test, validate unit test coverage, deploy" Explanation : Anypoint Platform supports continuous integration and continuous delivery using industry standard tools Mule Maven Plugin The Mule Maven plugin can automate building, packaging and deployment of Mule applications from source projects Using the Mule Maven plugin, you can automate your Mule application deployment to CloudHub, to Anypoint Runtime Fabric, or on-premises, using any of the following deployment strategies * CloudHub deployment * Runtime Fabric deployment * Runtime Manager REST API deployment * Runtime Manager agent deployment MUnit Maven Plugin The MUnit Maven plugin can automate test execution, and ties in with the Mule Maven plugin. It provides a full suite of integration and unit test capabilities, and is fully integrated with Maven and Surefire for integration with your continuous deployment environment. Since MUnit 2.x, the coverage report goal is integrated with the maven reporting section. Coverage Reports are generated during Maven's site lifecycle, during the coverage-report goal. One of the features of MUnit Coverage is to fail the build if a certain coverage level is not reached. MUnit is not used for integration testing Also publishing to Anypoint Exchange or to create associated API instances in API Manager is not a part of CICD pipeline which can ne achieved using mulesoft provided maven plugin Explanation:
Architecture mentioned in the question can be diagrammatically put as below. Persistent Object Store is the correct answer .
* Mule Object Stores: An object store is a facility for storing objects in or across Mule applications. Mule uses object stores to persist data for eventual retrieval.
Mule provides two types of object stores:
1) In-memory store - stores objects in local Mule runtime memory. Objects are lost on shutdown of the Mule runtime. So we cant use in memory store in our scenario as we want to share watermark within all cloudhub workers
2) Persistent store - Mule persists data when an object store is explicitly configured to be persistent. Hence this watermark will be available even any of the worker goes down Diagram Description automatically generated

NEW QUESTION # 143
What approach configures an API gateway to hide sensitive data exchanged between API consumers and API implementations, but can convert tokenized fields back to their original value for other API requests or responses, without having to recode the API implementations?

A. Create both masking and tokenization formats and use both to apply a tokenization policy in an API gateway to mask sensitive values in message payloads withcharacters, and apply a corresponding detokenization policy to return the original values to other APIs
B. Create a tokenization format and use it to apply a tokenization policy in an API gateway to replace sensitive fields in message payload with similarly formatted tokenized values, and apply a corresponding detokenization policy to return the original values to other APIs
C. Use a field-level encryption policy in an API gateway to replace sensitive fields in message payload with encrypted values, and apply a corresponding field-level decryption policy to return the original values to other APIs
D. Create a masking format and use it to apply a tokenization policy in an API gateway to mask sensitive values in message payloads with characters, and apply a corresponding detokenization policy to return the original values to other APIs

Answer: B

Explanation:
To hide sensitive data exchanged between API consumers and API implementations while allowing the conversion of tokenized fields back to their original values for other API requests or responses, the best approach is to use tokenization. This involves:
* Tokenization Format: Create a tokenization format that will be used to apply a tokenization policy in the API gateway. This format ensures that sensitive fields in message payloads are replaced with tokenized values that maintain a similar format, making them less recognizable as sensitive data.
* Tokenization Policy: Apply a tokenization policy in the API gateway that replaces sensitive data with tokenized values.
* Detokenization Policy: Apply a corresponding detokenization policy in the API gateway to convert tokenized values back to their original values when required by other APIs.
This method does not require recoding the API implementations and ensures that sensitive data is protected while still being accessible in its original form when necessary.
References
* MuleSoft Documentation on Tokenization Policies
* API Gateway Security Best Practices

NEW QUESTION # 144
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