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Amazon Exam MLS-C01 Topic 3 Question 82 Discussion

Actual exam question for Amazon's MLS-C01 exam
Question #: 82
Topic #: 3
[All MLS-C01 Questions]

A retail company is selling products through a global online marketplace. The company wants to use machine learning (ML) to analyze customer feedback and identify specific areas for improvement. A developer has built a tool that collects customer reviews from the online marketplace and stores them in an Amazon S3 bucket. This process yields a dataset of 40 reviews. A data scientist building the ML models must identify additional sources of data to increase the size of the dataset.

Which data sources should the data scientist use to augment the dataset of reviews? (Choose three.)

Show Suggested Answer Hide Answer
Suggested Answer: B, D, F

Contribute your Thoughts:

Dick
8 months ago
Ah, I see what you mean. I was also considering A and E, but those seem more focused on internal company data rather than external customer feedback. And F? I mean, instruction manuals? Not sure how that would help us understand customer sentiment.
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Stephaine
8 months ago
I agree, B, C, and D seem like the most relevant choices. Social media posts could give us a good sense of how customers are talking about the company and its products. News articles could provide some broader industry context. And a public collection of customer reviews would directly add to the existing dataset.
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Merissa
8 months ago
And a collection of customer reviews would directly add to the dataset.
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Johana
8 months ago
News articles could offer industry context.
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Jani
8 months ago
Agreed, social media posts can show how customers discuss the company.
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Ressie
8 months ago
I think B, C, and D are the best choices.
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Leatha
8 months ago
Okay, so we're looking for three data sources to augment the existing dataset. I'm thinking B, C, and D could be good options. What do you guys think?
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Felice
8 months ago
Hmm, this is an interesting question. I think the key here is to find additional sources of customer feedback that can complement the existing dataset of 40 reviews. Let's see what we have here...
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