Salesforce Exam MuleSoft Integration Architect I Topic 6 Question 26 Discussion

Actual exam question for Salesforce's MuleSoft Integration Architect I exam

Question #: 26
Topic #: 6

[All MuleSoft Integration Architect I Questions]

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?

AEvent-driven architecture

BMicroservice architecture

CAPI-led connectivity

DBatch-triggered ETL

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Suggested Answer: A

by Katina at Mar 06, 2025, 10:18 AM

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Erinn

1 months ago

API-led connectivity? More like API-led confusion if you ask me. Batch ETL all the way, folks. It's the classic choice for a reason.

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Gwen

18 days ago

Event-driven architecture might be too complex for this scenario, batch ETL seems like the safer bet.

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Louvenia

24 days ago

I agree, it's a reliable and efficient choice for this kind of integration.

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Clare

1 months ago

Batch ETL is definitely the way to go for handling large volumes of data.

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Deeanna

2 months ago

I'm not sure why anyone would even consider microservices for this use case. That's like trying to build a skyscraper with toothpicks!

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Tommy

15 days ago

D) Batch-triggered ETL

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Reynalda

18 days ago

C) API-led connectivity

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Miles

22 days ago

A) Event-driven architecture

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Irma

2 months ago

Hmm, I don't think an event-driven architecture would work well here. The high transaction volume and need for a daily snapshot make a batch-triggered ETL the logical choice in my opinion.

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Cordelia

2 months ago

I'm leaning towards option D. With millions of records and spikes in volume, a batch-triggered ETL process seems like the most efficient way to handle the data load without risking performance issues.

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