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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3 months ago

I agree with Joseph, option D seems like the most straightforward way to get the insights the data scientist is looking for. Plus, the horizontal line to represent the overall average is a nice touch.

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Luis

2 months ago

Definitely, the horizontal line will make it easier to see how each region performed relative to the average.

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Berry

2 months ago

I agree, having the average sales for each region compared to the overall average will provide a clear comparison.

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Cassi

2 months ago

I think option D is the best choice for visualizing the data trend.

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Marvel

3 months ago

I think option D is the most suitable as it shows average sales for each region faceted by year, providing a clear comparison.

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3 months ago

I think option D is the way to go. It'll give a clear visualization of the yearly average sales for each region, with the added horizontal line to show the overall average. This should make it easy to spot any regions that are performing above or below the average.

upvoted 0 times