Amazon MLS-C01 Exam - Topic 3 Question 121 Discussion

Actual exam question for Amazon's MLS-C01 exam

Question #: 121
Topic #: 3

[Exploratory Data Analysis]

A company wants to forecast the daily price of newly launched products based on 3 years of data for older product prices, sales, and rebates. The time-series data has irregular timestamps and is missing some values.

Data scientist must build a dataset to replace the missing values. The data scientist needs a solution that resamptes the data daily and exports the data for further modeling.

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

AUse Amazon EMR Serveriess with PySpark.

BUse AWS Glue DataBrew.

CUse Amazon SageMaker Studio Data Wrangler.

DUse Amazon SageMaker Studio Notebook with Pandas.

Show Suggested Answer

Suggested Answer: C

by Kaycee at Aug 14, 2025, 03:02 PM

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Tracie

3 months ago

EMR Serverless seems like overkill for this task.

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Rene

3 months ago

Not sure if DataBrew can handle all the missing values effectively.

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Elbert

3 months ago

Totally agree, DataBrew simplifies data prep!

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Lacey

4 months ago

Wait, can SageMaker Studio Data Wrangler really resample data like that?

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Sanjuana

4 months ago

I think AWS Glue DataBrew is the easiest for this.

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Lili

4 months ago

I recall that SageMaker Studio Data Wrangler is designed for data wrangling, but I wonder if it requires more setup than Glue DataBrew.

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Cordelia

4 months ago

I feel like we practiced a similar question where EMR was mentioned, but it seemed more complex than necessary for this task.

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Noel

4 months ago

I'm not entirely sure, but I think using Pandas in a SageMaker Notebook could be more flexible for handling missing values.

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Doretha

5 months ago

I remember we discussed AWS Glue DataBrew in class as a low-code solution for data preparation. It might be the easiest option here.

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Lindsey

5 months ago

Option A, the Amazon EMR Serverless with PySpark, seems like overkill for this task. I'd want to go with a more user-friendly, low-code solution like option C or B to get this done quickly and efficiently.

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Tammy

5 months ago

I think I'd lean towards option D, the Amazon SageMaker Studio Notebook with Pandas. That way I can have more control over the data transformation process and really dig into the details. Plus, I'm more familiar with Pandas than the other tools mentioned.

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Jesusa

5 months ago

Hmm, I'm a bit unsure about this one. The irregular timestamps and missing values make it seem a bit more complex. I'm wondering if option B, AWS Glue DataBrew, might be a better fit since it's specifically designed for data preparation tasks like this.

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Dalene

5 months ago

This looks like a pretty straightforward data preprocessing task. I'd probably go with option C - Amazon SageMaker Studio Data Wrangler. It seems like the easiest solution to resample the data and handle the missing values.

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Emile

6 months ago

But option C) specifically focuses on data wrangling, which is essential for this task.

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Dana

6 months ago

I disagree, I believe option A) is more efficient.

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Dortha

6 months ago

Option C looks like the way to go. SageMaker Studio Data Wrangler is designed for this kind of data prep and ETL task. Least implementation effort, for sure.

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