Amazon Exam MLS-C01 Topic 1 Question 72 Discussion

Actual exam question for Amazon's MLS-C01 exam

Question #: 72
Topic #: 1

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)

AUse Amazon SageMaker Ground Truth to sort the data into two groups named 'enrolled' or 'not enrolled.'

BUse a forecasting algorithm to run predictions.

CUse a regression algorithm to run predictions.

DUse a classification algorithm to run predictions

EUse the built-in Amazon SageMaker k-means algorithm to cluster the data into two groups named 'enrolled' or 'not enrolled.'

Show Suggested Answer

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

Limited Time Offer

25%

Off

Get Premium MLS-C01 Questions as Interactive Web-Based Practice Test or PDF

Contribute your Thoughts:

Submit Cancel

Brigette

1 months 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?

upvoted 0 times

Shad

10 days ago

C: I think using a forecasting algorithm might be too complex for predicting student enrollment.

upvoted 0 times

...

James

19 days ago

B: Yeah, I think using a classification algorithm would be more efficient for this task.

upvoted 0 times

...

Donte

27 days ago

A: I agree, using a classification algorithm would be simpler and more straightforward.

upvoted 0 times

...

Annice

1 months 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!

upvoted 0 times

Lindy

3 days ago

User 3: Using a forecasting algorithm could also help predict enrollment.

upvoted 0 times

...

Joesph

4 days ago

User 2: They could also use a classification algorithm to automate the process.

upvoted 0 times

...

Destiny

17 days ago

User 1: Haha, that does sound like a lot of work!

upvoted 0 times

...

Shala

20 days ago

D: Using a forecasting algorithm could also help in predicting enrollment trends.

upvoted 0 times

...

Hershel

23 days ago

C: They could also use Amazon SageMaker k-means algorithm to cluster the data.

upvoted 0 times

...

Lucia

24 days ago

B: Yeah, that would be more efficient than manually labeling each student.

upvoted 0 times

...

Junita

1 months ago

A: I think they should use a classification algorithm to predict enrollment.

upvoted 0 times

...

Hollis

2 months 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.

upvoted 0 times

Sabrina

28 days 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.

upvoted 0 times

...

Johna

1 months ago

A: I think you're right. Using k-means clustering could help us identify important characteristics of the 'enrolled' and 'not enrolled' groups.

upvoted 0 times

...

Mel

2 months 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.

upvoted 0 times

...