MLS-C01 · Question #196
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…
The correct answer is A. Emails exchanged by customers and the company's customer service agents B. Social media posts containing the name of the company or its products D. A publicly available collection of customer reviews. The goal is to analyze customer sentiment and feedback, so augmenting data requires sources that contain genuine customer opinions about the company or its products. Option A (customer service emails) contains direct, detailed feedback from real customers about pain points and…
Question
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.)
Options
- AEmails exchanged by customers and the company's customer service agents
- BSocial media posts containing the name of the company or its products
- CA publicly available collection of news articles
- DA publicly available collection of customer reviews
- EProduct sales revenue figures for the company
- FInstruction manuals for the company's products
How the community answered
(27 responses)- A89% (24)
- C4% (1)
- E7% (2)
Explanation
The goal is to analyze customer sentiment and feedback, so augmenting data requires sources that contain genuine customer opinions about the company or its products. Option A (customer service emails) contains direct, detailed feedback from real customers about pain points and problems. Option B (social media posts) captures organic, unsolicited customer opinions at scale - a rich signal for sentiment analysis. Option D (publicly available customer reviews) is semantically identical to the existing dataset and directly relevant. Option C (news articles) describes third-party reporting, not customer experiences - it would introduce noise rather than useful signal. Option E (sales revenue figures) is structured numerical data with no text sentiment content. Option F (instruction manuals) is product documentation authored by the company, not feedback from customers.
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