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Amazon MLS-C01 Exam - Topic 1 Question 72 Discussion

A university wants to develop a targeted recruitment strategy to increase new student enrollment. A data scientist gathers information about the academic performance history of students. The data scientist wants to use the data to build student profiles. The university will use the profiles to direct resources to recruit students who are likely to enroll in the university.Which combination of steps should the data scientist take to predict whether a particular student applicant is likely to enroll in the university? (Select TWO)
B) Use a forecasting algorithm to run predictions. and D) Use a classification algorithm to run predictions
A) Use Amazon SageMaker Ground Truth to sort the data into two groups named 'enrolled' or 'not enrolled.'
C) Use a regression algorithm to run predictions.
E) Use the built-in Amazon SageMaker k-means algorithm to cluster the data into two groups named 'enrolled' or 'not enrolled.'

Amazon MLS-C01 Exam - Topic 1 Question 72 Discussion

Actual exam question for Amazon's MLS-C01 exam
Question #: 72
Topic #: 1
[All MLS-C01 Questions]

A university wants to develop a targeted recruitment strategy to increase new student enrollment. A data scientist gathers information about the academic performance history of students. The data scientist wants to use the data to build student profiles. The university will use the profiles to direct resources to recruit students who are likely to enroll in the university.

Which combination of steps should the data scientist take to predict whether a particular student applicant is likely to enroll in the university? (Select TWO)

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Suggested Answer: B, D

https://www.redhat.com/en/topics/devops/what-is-blue-green-deployment

by Bobbye at Mar 25, 2023, 04:10 PM

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Kanisha
7 months ago
I don’t know, using regression seems risky for this type of data.
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Aron
7 months ago
Totally agree with D! Classification is key here.
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Catina
7 months ago
Wait, can you really use k-means for this? Seems off.
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Sherman
8 months ago
I think A is a good choice too, but not sure about E.
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Fernanda
8 months ago
Definitely B and D for predictions!
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Isaiah
8 months ago
I feel like clustering with k-means might not be the best approach since we need to predict outcomes, not just group data.
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Buddy
8 months ago
I practiced a similar question where we had to classify data, and I think using Amazon SageMaker for sorting could be helpful, but I'm not sure if it's the right step.
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Antonio
8 months ago
I'm not entirely sure, but I remember something about regression being used for continuous outcomes, so it might not be the best fit here.
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Joanna
8 months ago
I think we should use a classification algorithm to predict enrollment since we're dealing with categories like 'enrolled' or 'not enrolled.'
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Wei
8 months ago
I vaguely recall something about clustering methods like k-means, but I don't think it fits this scenario since we need to predict enrollment, not just group data. So, E seems off.
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Vince
8 months ago
I practiced a similar question where we had to classify data into categories. I think using a classification algorithm is definitely the way to go, so D seems solid.
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Elouise
8 months ago
I'm a bit unsure about the best approach here. I feel like regression could work too, but isn't it more for continuous outcomes? Maybe option C isn't the right fit?
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Marshall
8 months ago
I remember we discussed the importance of classification algorithms for binary outcomes like enrollment status. I think option D might be a good choice.
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Frank
8 months ago
Hmm, this looks familiar, but I want to make sure I get the order right. I'll think it through step-by-step.
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Mollie
8 months ago
Ah, I think I know this one! Participatory design involves users throughout the process, so the answer is likely A - discovery, evaluation, and prototype.
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Eun
8 months ago
This one seems pretty straightforward. I think the answer is Remote service observation, since that's a common way to monitor calls in a Service Desk.
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Luis
8 months ago
Wait, is CPR the right acronym here? I'm not totally sure what that stands for in this context.
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Brigette
1 year ago
I don't know, guys. Using a forecasting algorithm to predict enrollment seems a bit overkill. Why not just use a good old-fashioned classification algorithm and call it a day?
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Shad
1 year ago
C: I think using a forecasting algorithm might be too complex for predicting student enrollment.
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James
1 year ago
B: Yeah, I think using a classification algorithm would be more efficient for this task.
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Donte
1 year ago
A: I agree, using a classification algorithm would be simpler and more straightforward.
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Annice
1 year ago
Haha, I can just imagine the data scientist using Amazon SageMaker Ground Truth to manually label each student as 'enrolled' or 'not enrolled'. Talk about a labor-intensive approach!
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Barney
1 year ago
Joesph: True, combining different algorithms could give more accurate results.
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Lindy
1 year ago
User 3: Using a forecasting algorithm could also help predict enrollment.
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Joesph
1 year ago
User 2: They could also use a classification algorithm to automate the process.
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Destiny
1 year ago
User 1: Haha, that does sound like a lot of work!
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Shala
1 year ago
D: Using a forecasting algorithm could also help in predicting enrollment trends.
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Hershel
1 year ago
C: They could also use Amazon SageMaker k-means algorithm to cluster the data.
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Lucia
1 year ago
B: Yeah, that would be more efficient than manually labeling each student.
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Junita
1 year ago
A: I think they should use a classification algorithm to predict enrollment.
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Hollis
1 year ago
I'm not so sure about that. Shouldn't we be using something like k-means clustering to group the students first before running any predictions? That could give us better insights into the key characteristics of the 'enrolled' and 'not enrolled' groups.
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Sabrina
1 year ago
B: Yeah, that makes sense. We can then use a classification algorithm to predict whether a particular student is likely to enroll based on those key characteristics.
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Johna
1 year ago
A: I think you're right. Using k-means clustering could help us identify important characteristics of the 'enrolled' and 'not enrolled' groups.
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Mel
1 year ago
I think options C and D are the way to go. Using a regression algorithm to predict enrollment probability and a classification algorithm to directly classify students as 'enrolled' or 'not enrolled' seems like the most logical approach.
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Merilyn
1 year ago
I'm not sure. Maybe we should also consider using Amazon SageMaker Ground Truth to sort the data first.
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Tandra
1 year ago
I agree with Eden. Using a classification algorithm would be the best approach.
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Eden
1 year ago
I think we should use a classification algorithm to predict enrollment.
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