Exam AWS Certified Machine Learning - Specialty topic 1 question 300 discussion

Correct AD

first, classify the student profiles. then use classification algorithm to run predictions

K-means is unsupervised, so not useful for clustering. For grouping, use GroundTruth. It’s a classification problem. So, A and D are right.

This question is focusing on either yes/no type of response (binary). So I think Classification algorithm would work the best as compared to K-means which is solely responsible for clustering the data.

D. Use a classification algorithm to run predictions: This approach is suitable for binary outcomes, such as predicting whether a student will enroll ("enrolled") or not ("not enrolled"). A. Use Amazon SageMaker Ground Truth to sort the data into two groups named "enrolled" or "not enrolled.": This service can help in labeling the dataset accurately, providing a strong foundation for training the classification model.

The data scientist should use Amazon SageMaker Ground Truth to sort the data into two groups named "enrolled" or "not enrolled." This will create a labeled dataset that can be used for supervised learning. The data scientist should then use a classification algorithm to run predictions on the test data. A classification algorithm is a suitable choice for predicting a binary outcome, such as enrollment status, based on the input features, such as academic performance. A classification IT Certification Guaranteed, The Easy Way! 163 algorithm will output a probability for each class label and assign the most likely label to each observation. References: Use Amazon SageMaker Ground Truth to Label Data Classification Algorithm in Machine Learning

It mentions combination of options. It is a classification problem and labels will be needed.