Amazon MLS-C01 Exam - Topic 2 Question 100 Discussion

Actual exam question for Amazon's MLS-C01 exam

Question #: 100
Topic #: 2

A data scientist uses Amazon SageMaker Data Wrangler to define and perform transformations and feature engineering on historical dat

a. The data scientist saves the transformations to SageMaker Feature Store.

The historical data is periodically uploaded to an Amazon S3 bucket. The data scientist needs to transform the new historic data and add it to the online feature store The data scientist needs to prepare the .....historic data for training and inference by using native integrations.

Which solution will meet these requirements with the LEAST development effort?

AUse AWS Lambda to run a predefined SageMaker pipeline to perform the transformations on each new dataset that arrives in the S3 bucket.

BRun an AWS Step Functions step and a predefined SageMaker pipeline to perform the transformations on each new dalaset that arrives in the S3 bucket

CUse Apache Airflow to orchestrate a set of predefined transformations on each new dataset that arrives in the S3 bucket.

DConfigure Amazon EventBridge to run a predefined SageMaker pipeline to perform the transformations when a new data is detected in the S3 bucket.

Show Suggested Answer

Suggested Answer: D

The best solution is to configure Amazon EventBridge to run a predefined SageMaker pipeline to perform the transformations when a new data is detected in the S3 bucket. This solution requires the least development effort because it leverages the native integration between EventBridge and SageMaker Pipelines, which allows you to trigger a pipeline execution based on an event rule. EventBridge can monitor the S3 bucket for new data uploads and invoke the pipeline that contains the same transformations and feature engineering steps that were defined in SageMaker Data Wrangler. The pipeline can then ingest the transformed data into the online feature store for training and inference.

The other solutions are less optimal because they require more development effort and additional services. Using AWS Lambda or AWS Step Functions would require writing custom code to invoke the SageMaker pipeline and handle any errors or retries. Using Apache Airflow would require setting up and maintaining an Airflow server and DAGs, as well as integrating with the SageMaker API.

References:

Amazon EventBridge and Amazon SageMaker Pipelines integration

Create a pipeline using a JSON specification

Ingest data into a feature group

by Hildegarde at Aug 27, 2024, 10:00 PM

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Dottie

3 months ago

I disagree, using Apache Airflow feels unnecessary for this scenario.

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Timmy

3 months ago

A is pretty straightforward too, but I prefer D.

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Dannette

3 months ago

Wait, can EventBridge really trigger SageMaker pipelines? That's cool!

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Twanna

4 months ago

I think B might be overkill for this task.

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Nicolette

4 months ago

Option D seems like the easiest way to automate this!

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Alverta

4 months ago

I vaguely recall that Apache Airflow is great for orchestration, but it might require more setup than just using SageMaker directly.

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Corrie

4 months ago

I practiced a similar question, and I feel like AWS Step Functions could add complexity that isn't necessary here.

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Ciara

4 months ago

I think using Amazon EventBridge sounds familiar; it might be the easiest way to automate the pipeline when new data arrives.

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Leatha

5 months ago

I remember studying how AWS Lambda can trigger processes based on S3 events, but I'm not sure if it's the best option for this scenario.

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Georgiann

5 months ago

I'm leaning towards option B with Step Functions. The ability to orchestrate a series of transformations in a workflow seems well-suited for this use case. Plus, I'm more familiar with Step Functions, so I feel confident I could implement that solution efficiently.

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Kami

5 months ago

Hmm, I'm a bit unsure about this one. The question mentions "native integrations", so I'm wondering if one of the other options like Step Functions or Airflow might be a better fit to leverage those integrations. I'll need to think this through a bit more.

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Deja

5 months ago

This seems like a pretty straightforward question. I'd go with option A - using AWS Lambda to run a predefined SageMaker pipeline. It looks like the most direct way to automate the transformations on new data as it arrives.

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Esteban

5 months ago

Option D with EventBridge sounds interesting. I like the idea of having an event-driven approach to trigger the transformations automatically. That could help reduce the manual effort required. I'll make sure to read through the details of each choice carefully.

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Filiberto

5 months ago

Hmm, I'm a little unsure about this one. I know effective listening involves things like maintaining eye contact and nodding to show you're engaged, but I'm not totally sure which of these options is the best answer.

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Martha

1 year ago

I agree with Carey, Amazon EventBridge can trigger the transformations automatically with less effort.

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Lynette

1 year ago

Option D all the way! Easy peasy, just like my grandma's apple pie. Now, where's the feature store to put all those delicious data transformations?

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Jeannetta

1 year ago

Then you can prepare the historic data for training and inference using native integrations.

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Johnetta

1 year ago

Yeah, you can save the transformations to SageMaker Feature Store.

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Melissa

1 year ago

I think the feature store is in SageMaker, where you can save the transformations.

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Jill

1 year ago

Option D all the way! Easy peasy, just like my grandma's apple pie.

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Remona

1 year ago

But option A involves using AWS Lambda, which can automate the process.

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Carey

1 year ago

I disagree, I believe option D is more efficient.

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Remona

1 year ago

I think option A is the best choice.

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Audra

1 year ago

Personally, I'd go with Option D. Why complicate things with Lambda, Step Functions, or Airflow when EventBridge can do the job elegantly?

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Emeline

1 year ago

I agree, Option D with Amazon EventBridge seems like the most efficient choice here.

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Romana

1 year ago

Option D is definitely the way to go. It's the simplest solution for this scenario.

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Gennie

1 year ago

Option C with Apache Airflow seems a bit overkill for this use case. EventBridge is probably the simplest and most efficient solution.

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King

1 year ago

I'm partial to Option B with AWS Step Functions. It gives you a bit more control and visibility over the workflow compared to just using EventBridge.

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Bette

1 year ago

I agree, it provides more control and visibility over the workflow.

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Clorinda

1 year ago

Option B with AWS Step Functions sounds like a good choice.

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Coral

2 years ago

Option D looks like the most straightforward way to handle this. EventBridge can trigger the SageMaker pipeline automatically when new data arrives in the S3 bucket.

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Sabine

1 year ago

Yes, it definitely simplifies the workflow for the data scientist.

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Rikki

1 year ago

I agree, using Amazon EventBridge to automate the process seems efficient.

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Dorathy

1 year ago

Option D looks like the most straightforward way to handle this. EventBridge can trigger the SageMaker pipeline automatically when new data arrives in the S3 bucket.

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