Exam AWS Certified Machine Learning - Specialty All Questions

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Exam AWS Certified Machine Learning - Specialty topic 1 question 121 discussion

Exam question from Amazon's AWS Certified Machine Learning - Specialty

Question #: 121
Topic #: 1

[All AWS Certified Machine Learning - Specialty Questions]

A data scientist uses an Amazon SageMaker notebook instance to conduct data exploration and analysis. This requires certain Python packages that are not natively available on Amazon SageMaker to be installed on the notebook instance.
How can a machine learning specialist ensure that required packages are automatically available on the notebook instance for the data scientist to use?

A. Install AWS Systems Manager Agent on the underlying Amazon EC2 instance and use Systems Manager Automation to execute the package installation commands.
B. Create a Jupyter notebook file (.ipynb) with cells containing the package installation commands to execute and place the file under the /etc/init directory of each Amazon SageMaker notebook instance.
C. Use the conda package manager from within the Jupyter notebook console to apply the necessary conda packages to the default kernel of the notebook.
D. Create an Amazon SageMaker lifecycle configuration with package installation commands and assign the lifecycle configuration to the notebook instance.

Show Suggested Answer

Suggested Answer: D 🗳️

by [deleted] at Feb. 5, 2021, 7:56 p.m.

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Highly Voted 2 years, 9 months ago

I would select D. See AWS documentation: https://docs.aws.amazon.com/sagemaker/latest/dg/nbi-add-external.html

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u_b

Most Recent 7 months, 2 weeks ago

by excluding wrong options: A you might not have access to the EC2 instance => out B no automation => out C only the default kernel, which limits the DS => out => D

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Mickey321

10 months, 1 week ago

Selected Answer: D

option D

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ADVIT

12 months ago

D. https://docs.aws.amazon.com/sagemaker/latest/dg/nbi-add-external.html

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CKS1210

1 year ago

Selected Answer: D

Install custom environments and kernels on the notebook instance's Amazon EBS volume. This ensures that they persist when you stop and restart the notebook instance, and that any external libraries you install are not updated by SageMaker. To do that, use a lifecycle configuration that includes both a script that runs when you create the notebook instance (on-create) and a script that runs each time you restart the notebook instance (on-start).

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31Rishab

1 year, 9 months ago

Selected Answer: D

Even the link given suggest Option D

upvoted 3 times

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ovokpus

2 years ago

Selected Answer: D

Please ignore my previous comment, the answer is D

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ovokpus

2 years ago

Selected Answer: B

Key word here is how can the developer "guarantee"?? He guarantees that by including the install commands as part of the notebook. So, against the grain, I stand with B

upvoted 1 times

ovokpus

2 years ago

Scratch that. The Answer is D

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ayatkhrisat

2 years, 1 month ago

Selected Answer: D

D should be the answer

upvoted 3 times

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vetaal

2 years, 5 months ago

Selected Answer: D

https://docs.aws.amazon.com/sagemaker/latest/dg/nbi-add-external.html upvoted 1 times

upvoted 2 times

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geekgirl007

2 years, 5 months ago

Selected Answer: D

D "automatically" is the key here and using lifecycle configuration https://docs.aws.amazon.com/sagemaker/latest/dg/nbi-add-external.html

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rafaelo

2 years, 6 months ago

It is D, although is not even the best answer in my opinion. Although by default conda packages are installed in ephemeral storage, you can change that default behaviour. I did that in my last project and we created our own conda environment that persisted between shutdowns.

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abdohanfi

2 years, 8 months ago

based on the refernce given under the answer its D not B

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mona_mansour

2 years, 8 months ago

You can install packages using the following methods: 1-Lifecycle configuration scripts 2-Notebooks – The following commands are supported. %conda install %pip install 3-The Jupyter terminal – You can install packages using pip and conda directly.

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