Amazon Exam MLS-C01 Topic 2 Question 89 Discussion

Actual exam question for Amazon's MLS-C01 exam

Question #: 89
Topic #: 2

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 Tiffiny at Apr 08, 2024, 11:15 PM

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Zona

10 months ago

I see the benefits of both options A and B. However, I think adding a horizontal line in each facet, as suggested in option B, will make it easier to compare regions against the overall average.

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Oretha

10 months ago

I personally prefer option B. Color coding by region can provide additional insights into how each region is performing compared to the average.

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Hoa

10 months ago

I agree with Juliann. Creating a bar plot faceted by year will help in identifying any trends in sales performance.

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Juliann

10 months ago

I think option A is the best choice. It will give a clear comparison of average sales for each store over the years.

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Tomoko

11 months ago

I see your point, However, option D focuses on comparing regional performance which could be more valuable for the company.

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Ciara

11 months ago

But in option B, we can see the average sales for each store and compare them across years.

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Dorothy

11 months ago

I disagree, I believe option D would provide a clearer comparison of regional performance.

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Ciara

11 months ago

I think option B could be the best visualization for this scenario.

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Audra

1 years ago

You know, I was leaning towards option C at first, but now I'm not so sure. Creating an aggregated dataset by region might be a bit too high-level. The data scientist might want to see the store-level data as well, to get a better sense of the variation within each region.

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Marjory

1 years ago

I'm a little torn between options C and D, to be honest. I like the idea of the simple bar plot in option C, but I think the faceted layout in option D will give the data scientist a more comprehensive view of the data. Plus, adding that horizontal line to represent the overall average is a nice touch.

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Antonette

1 years ago

Haha, you know what they say - a picture is worth a thousand sales numbers! But in all seriousness, I think option D is the way to go. The data scientist will be able to see at a glance which regions are performing above or below the company average. Plus, the faceted layout will make it easy to spot any year-over-year changes.

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France

1 years ago

Ooh, that's an interesting idea! A line plot could definitely work too. Though I do think the bar plot options might be a bit more visually appealing, especially if they use some nice colors to differentiate the regions.

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Rima

1 years ago

I agree, option D is definitely the way to go. Having the faceted bar plot will make it easy to spot any trends or outliers in the regional sales data. Plus, adding that horizontal line to show the overall average is a nice touch that will really help the data scientist benchmark each region's performance.

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Maddie

1 years ago

Ha, I can just imagine the data scientist staring at a bunch of bar plots, trying to make sense of it all. Maybe they should just go with a nice, simple line plot? That way they can see the trends over time for each region more clearly.

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Xochitl

1 years ago

This is a great question! It really tests our understanding of data visualization techniques and how to effectively analyze sales data. I think option D is the best choice here. Creating a bar plot faceted by year, with average sales for each region and a horizontal line to represent the overall average, will give the data scientist a clear visual of how each region is performing compared to the company-wide average.

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Edda

11 months ago

In the end, I still lean towards option D for a more straightforward comparison of regional sales to the company-wide average.

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Dusti

11 months ago

True, option B might provide a more detailed view of sales performance across different regions.

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Joanna

12 months ago

I think option B could work well too, especially if the data scientist wants to see how sales vary by region.

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Laura

12 months ago

What about option B? Creating a bar plot colored by region could give a clearer picture of sales trends.

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Belen

12 months ago

I see your point, but I still think option D is better for comparing each region to the overall average.

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Ressie

12 months ago

I think option A could also be a good choice, creating a bar plot faceted by year for each store.

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Minna

12 months ago

I agree, option D sounds like the best choice for analyzing the sales data.

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Lucia

1 years ago

Hmm, I'm not sure. Option B also seems like it could work, with a bar plot colored by region and faceted by year. That might make it easier to compare each region's performance year-over-year. I'd have to think about the pros and cons of each approach.

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Eleni

1 years ago

I agree, this is a good question. I'm leaning towards option D. Creating a bar plot faceted by year, with each region's average sales, and adding a horizontal line to represent the overall average sales. That seems like it would give the data scientist a clear picture of how each region is performing compared to the average.

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Tony

10 months ago

I see your point, Michel. Option B might provide a clearer distinction between regions when comparing sales.

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Michel

11 months ago

I prefer option B. Color-coding by region can help visualize the performance of each region better.

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Lore

11 months ago

I agree with Pura, option A seems like a good choice. It will show how each store is doing over the years.

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Pura

11 months ago

I think option A could also be helpful. Creating a bar plot faceted by year for each store's average sales sounds informative.

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Farrah

1 years ago

I think this is a great question that really tests our ability to analyze data and choose the right visualization. The data scientist wants to understand yearly average sales for each region and how each region compares to the overall average. I think the key is to choose a visualization that makes those insights clear and easy to interpret.

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