AWS Certified Machine Learning Engineer Associate · Free Practice Question Medium
Question 28
A machine learning engineer is setting up a Jupyter notebook environment to streamline an end-to-end machine learning workflow, including model training and deployment. The engineer wants to minimize manual infrastructure management and use a fully integrated environment within Amazon SageMaker.
Which option should the engineer choose to achieve this?
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A
Set up a SageMaker notebook instance from the AWS Management Console and attach an IAM role.
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B
Manually provision an EC2 instance with Jupyter notebooks installed and manage the environment through AWS CLI.
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C
Install SageMaker libraries on a local machine and run Jupyter notebooks locally.
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D
Use SageMaker Studio to set up a domain and configure multiple Jupyter Lab notebooks within the same workspace.
Reveal correct answer
Correct answer: D
A.
While a SageMaker notebook instance allows you to perform ML tasks, it operates independently and doesn’t offer the multi-notebook workspace of Studio, making it less seamless for integrated projects.
B.
Manually managing EC2 instances increases complexity and infrastructure management overhead, which contradicts the engineer’s goal.
C.
Running Jupyter notebooks locally on a machine requires manual setup and does not take advantage of SageMaker’s managed infrastructure and integration.
D.
SageMaker Studio provides a fully managed, integrated environment where multiple Jupyter Lab notebooks can be configured, with persistent storage and simplified management of ML tasks, ideal for the described scenario.
Discussion
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