Amazon Exam MLS-C01 Topic 4 Question 101 Discussion

Actual exam question for Amazon's MLS-C01 exam

Question #: 101
Topic #: 4

A data scientist is building a forecasting model for a retail company by using the most recent 5 years of sales records that are stored in a data warehouse. The dataset contains sales records for each of the company's stores across five commercial regions The data scientist creates a working dataset with StorelD. Region. Date, and Sales Amount as columns. The data scientist wants to analyze yearly average sales for each region. The scientist also wants to compare how each region performed compared to average sales across all commercial regions.

Which visualization will help the data scientist better understand the data trend?

ACreate an aggregated dataset by using the Pandas GroupBy function to get average sales for each year for each store. Create a bar plot, faceted by year, of average sales for each store. Add an extra bar in each facet to represent average sales.

BCreate an aggregated dataset by using the Pandas GroupBy function to get average sales for each year for each store. Create a bar plot, colored by region and faceted by year, of average sales for each store. Add a horizontal line in each facet to represent average sales.

CCreate an aggregated dataset by using the Pandas GroupBy function to get average sales for each year for each region Create a bar plot of average sales for each region. Add an extra bar in each facet to represent average sales.

DCreate an aggregated dataset by using the Pandas GroupBy function to get average sales for each year for each region Create a bar plot, faceted by year, of average sales for each region Add a horizontal line in each facet to represent average sales.

Show Suggested Answer

Suggested Answer: D

The best visualization for this task is to create a bar plot, faceted by year, of average sales for each region and add a horizontal line in each facet to represent average sales. This way, the data scientist can easily compare the yearly average sales for each region with the overall average sales and see the trends over time. The bar plot also allows the data scientist to see the relative performance of each region within each year and across years. The other options are less effective because they either do not show the yearly trends, do not show the overall average sales, or do not group the data by region.

References:

pandas.DataFrame.groupby --- pandas 2.1.4 documentation

pandas.DataFrame.plot.bar --- pandas 2.1.4 documentation

Matplotlib - Bar Plot - Online Tutorials Library

by Mary at Sep 11, 2024, 01:08 PM

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Xuan

10 days ago

Personally, I'd go with option D. It's clean, it's clear, and it gives the data scientist exactly what they need to understand the regional sales trends. Can't go wrong with that.

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Devora

17 days ago

Haha, option A with the extra bar in each facet to represent the average? That's like a visual dad joke. I mean, it might work, but it seems a little gimmicky.

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Dean

8 days ago

User 1: I think option A is a bit gimmicky with the extra bar for average sales.

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1 months ago

Hmm, I'm not sure. Option B looks like it could be useful too, with the bar plot colored by region and faceted by year. That might help the data scientist see how each region is trending over time compared to the others.

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Amira

3 days ago

True, Option B could provide a clear comparison of how each region is performing compared to the others.

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Stefany

5 days ago

But Option B also seems interesting, with the bar plot colored by region to compare performance across regions.

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Lauran

6 days ago

I agree, Option A seems like a good choice to analyze yearly average sales for each store.

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Sarina

14 days ago

I think Option A could work well, with the bar plot faceted by year for each store.

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1 months ago

I think option A is the best choice because it shows average sales for each store faceted by year.

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