Salesforce Certified MuleSoft Platform Integration Architect (Mule-Arch-202) Exam - Topic 6 Question 26 Discussion

Actual exam question for Salesforce's Salesforce Certified MuleSoft Platform Integration Architect (Mule-Arch-202) exam

Question #: 26
Topic #: 6

[All Salesforce Certified MuleSoft Platform Integration Architect (Mule-Arch-202) 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

Show Suggested Answer

Suggested Answer: A

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

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Mirta

8 months ago

Definitely leaning towards Batch-triggered ETL for efficiency!

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Trinidad

9 months ago

Wait, are we sure tens of millions of records can be processed daily?

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Katina

9 months ago

Event-driven architecture might be overkill for this use case.

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Youlanda

9 months ago

Sounds like Batch-triggered ETL is the way to go for daily snapshots.

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Heike

9 months ago

I think API-led connectivity could handle the spikes better.

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Zana

10 months ago

Batch-triggered ETL seems to align with the requirement for a daily CSV file, but I wonder if it can handle the spikes effectively.

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Yoko

10 months ago

I practiced a similar question where API-led connectivity was mentioned, but I'm not convinced it's the best option for daily snapshots.

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Dorthy

10 months ago

I'm not entirely sure, but I feel like event-driven architecture could handle spikes in transaction volume better.

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Johnna

10 months ago

I remember studying about batch processing for large volumes of data, so I think Batch-triggered ETL might be a good fit here.

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Gracia

10 months ago

Event-driven architecture seems like an interesting option, but I'm not sure it's the most efficient way to handle the scale and frequency of the data updates. I'd lean more towards a batch-triggered ETL approach.

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Leigha

11 months ago

Based on the details provided, I think a batch-triggered ETL solution would be the most appropriate. It can handle the high volume and spikes, and the daily snapshot requirement fits well with a batch process.

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Golda

11 months ago

I'm a bit confused here. Microservices or API-led connectivity could also work, but I'd need to understand more about the legacy system and the data warehouse requirements.

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Alise

11 months ago

Hmm, not sure about that. With the daily snapshot requirement, an event-driven architecture might be a better fit to capture the changes in real-time.

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Penney

11 months ago

This looks like a classic data integration problem. I'd probably go with a batch-triggered ETL approach to handle the high transaction volume and spikes.

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Erinn

1 year 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

1 year ago

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

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Louvenia

1 year ago

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

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Clare

1 year ago

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

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Deeanna

1 year 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

1 year ago

D) Batch-triggered ETL

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Reynalda

1 year ago

C) API-led connectivity

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Miles

1 year ago

A) Event-driven architecture

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Irma

1 year 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

1 year 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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