Deal of The Day! Hurry Up, Grab the Special Discount - Save 25% - Ends In 00:00:00 Coupon code: SAVE25
Welcome to Pass4Success

- Free Preparation Discussions
Mail Us support@pass4success.com
Location
Mob_nav_barsMENU

Amazon Exam MLS-C01 Topic 3 Question 121 Discussion

Actual exam question for Amazon's MLS-C01 exam
Question #: 121
Topic #: 3
[All MLS-C01 Questions]

[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?

Show Suggested Answer Hide Answer
Suggested Answer: C

by Kaycee at Aug 14, 2025, 04:02 PM

Limited Time Offer

25%

Off

Get Premium MLS-C01 Questions as Interactive Web-Based Practice Test or PDF

Contribute your Thoughts:

Submit Cancel
Cordelia
2 days ago
I feel like we practiced a similar question where EMR was mentioned, but it seemed more complex than necessary for this task.
upvoted 0 times
...
Noel
8 days ago
I'm not entirely sure, but I think using Pandas in a SageMaker Notebook could be more flexible for handling missing values.
upvoted 0 times
...
Doretha
13 days 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.
upvoted 0 times
...
Lindsey
18 days 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.
upvoted 0 times
...
Tammy
23 days 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.
upvoted 0 times
...
Jesusa
28 days 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.
upvoted 0 times
...
Dalene
1 month 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.
upvoted 0 times
...
Emile
1 month ago
But option C) specifically focuses on data wrangling, which is essential for this task.
upvoted 0 times
...
Dana
2 months ago
I disagree, I believe option A) is more efficient.
upvoted 0 times
...
Dortha
2 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.
upvoted 0 times
...
Emile
3 months ago
I think option C) is the best choice.
upvoted 0 times
...

Save Cancel