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Amazon MLS-C01 Exam - Topic 2 Question 119 Discussion

[Modeling]A company sells thousands of products on a public website and wants to automatically identify products with potential durability problems. The company has 1.000 reviews with date, star rating, review text, review summary, and customer email fields, but many reviews are incomplete and have empty fields. Each review has already been labeled with the correct durability result.A machine learning specialist must train a model to identify reviews expressing concerns over product durability. The first model needs to be trained and ready to review in 2 days.What is the MOST direct approach to solve this problem within 2 days?
A) Train a custom classifier by using Amazon Comprehend.
B) Build a recurrent neural network (RNN) in Amazon SageMaker by using Gluon and Apache MXNet.
C) Train a built-in BlazingText model using Word2Vec mode in Amazon SageMaker.
D) Use a built-in seq2seq model in Amazon SageMaker.

Amazon MLS-C01 Exam - Topic 2 Question 119 Discussion

Actual exam question for Amazon's MLS-C01 exam
Question #: 119
Topic #: 2
[All MLS-C01 Questions]

[Modeling]

A company sells thousands of products on a public website and wants to automatically identify products with potential durability problems. The company has 1.000 reviews with date, star rating, review text, review summary, and customer email fields, but many reviews are incomplete and have empty fields. Each review has already been labeled with the correct durability result.

A machine learning specialist must train a model to identify reviews expressing concerns over product durability. The first model needs to be trained and ready to review in 2 days.

What is the MOST direct approach to solve this problem within 2 days?

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Suggested Answer: A

by Lavina at Jul 14, 2025, 12:35 PM

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Annice
5 months ago
Wait, can we really train a model in just 2 days?
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Quentin
6 months ago
Custom classifiers can be hit or miss, though.
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Laila
6 months ago
Definitely go with BlazingText, it’s the best option here!
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Alayna
6 months ago
I think an RNN might be overkill for this.
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Charisse
6 months ago
BlazingText is super fast for text classification!
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Margurite
7 months ago
BlazingText sounds familiar; I recall it being efficient for text data. It might be the quickest way to get results.
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Jess
7 months ago
I think we practiced something similar with RNNs, but I'm worried about the time it takes to train them.
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Reid
7 months ago
I'm not sure if Amazon Comprehend is the best choice here, but it seems like a quick option to get started.
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Olene
7 months ago
I remember we discussed using pre-trained models for text classification, which might save time.
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Hobert
7 months ago
Hmm, I'm not sure a seq2seq model in option D is the right fit for this problem. It seems like overkill when we just need to classify the reviews as expressing durability concerns or not. I'd probably go with option A or C for the most direct approach.
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Olen
8 months ago
Given the tight timeline of 2 days, I think option C using the built-in BlazingText model in Amazon SageMaker would be the fastest way to get a working model. The Word2Vec mode should be able to handle the text data effectively.
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Amber
8 months ago
I'm a bit unsure about the best approach here. The data has some missing fields, so I'm not sure if a simple classifier would work well. Option B with a recurrent neural network might be a better fit to handle the unstructured text data.
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Erick
8 months ago
This seems like a straightforward text classification problem, so I think option A using Amazon Comprehend would be the most direct approach to get a model up and running quickly.
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Candida
10 months ago
If I were the machine learning specialist, I'd be tempted to just throw a coin to decide the answer. But hey, at least they're not asking us to build a self-driving car in a weekend!
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Leonida
10 months ago
Haha, the company better hope their products are more durable than this machine learning model they're building in 2 days! Gotta love those tight deadlines.
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Shalon
8 months ago
D: Use a built-in seq2seq model in Amazon SageMaker.
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Jose
8 months ago
C: Train a built-in BlazingText model using Word2Vec mode in Amazon SageMaker.
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Amie
8 months ago
B: Build a recurrent neural network (RNN) in Amazon SageMaker by using Gluon and Apache MXNet.
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Dominque
9 months ago
A: Train a custom classifier by using Amazon Comprehend.
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Merilyn
10 months ago
I'm not sure if a seq2seq model is the best fit for this task. It's typically used for tasks like machine translation, not sentiment analysis. I think the BlazingText model is the way to go here.
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Becky
8 months ago
I see your point about the seq2seq model not being the best fit. The BlazingText model does seem like a more suitable choice for this specific problem.
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Carlton
10 months ago
I think using Amazon Comprehend to train a custom classifier could also be a good option for quickly identifying product durability concerns.
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Linsey
10 months ago
I agree, the BlazingText model using Word2Vec mode seems like the most appropriate choice for this task.
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Darnell
10 months ago
I see the rationale behind using Amazon Comprehend, but I think training a built-in BlazingText model in Amazon SageMaker could also be a quick and effective solution.
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Buck
10 months ago
I would go with building a recurrent neural network in Amazon SageMaker using Gluon and Apache MXNet for more customization.
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Dante
11 months ago
The BlazingText model in Amazon SageMaker seems like the quickest and most direct approach to solve this problem within the 2-day timeline. It's a built-in model, so it should be faster to train than building a custom RNN.
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Jamey
10 months ago
B: Yeah, it's a built-in model so it should be quicker to train than building a custom RNN.
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Mel
10 months ago
A: I think using the BlazingText model in Amazon SageMaker is the way to go.
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Lorean
11 months ago
I agree with Tony, Amazon Comprehend seems like the most direct approach to solve the problem quickly.
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Tony
11 months ago
I think the best approach would be to train a custom classifier using Amazon Comprehend.
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