Scala Kernel for Jupyter Notebooks

Answer: D.

Option A is incorrect.

The default list of available images in SageMaker studio does not include a scala image.

Option B is incorrect.

SageMaker Studio images are built on Docker images, not AMIs.

Option C is incorrect.

While you should create a custom SageMaker docker image using the Scala kernel based on the Almond Scala Kernel SageMaker custom image sample, attaching it to each user's profile is less efficient than attaching it to the SageMaker Studio domain.

Option D is CORRECT.

The most efficient option is to create a custom SageMaker docker image using the Scala kernel based on the Almond Scala Kernel SageMaker custom image sample and attach it to the SageMaker Studio domain.

Reference:

Please see the Amazon Sagemaker developer guide titled Bring your own SageMaker image.

Please refer to the GitHub repository titled SageMaker Studio Custom Image Samples.

As a machine learning specialist, you are building a machine learning model for managing risk for your firm using data from your traders' daily trading activity. In the stage of your model development, you need to provide Jupyter notebooks to your development team that they can use in SageMaker Studio. The developers need to use the Scala kernel based on the Almond Scala Kernel as their development environment in their Jupyter notebooks. You want to provide the required development environment to your developers for their Jupyter notebooks in the most efficient manner.

The most efficient manner to provide the required development environment to your developers for their Jupyter notebooks in SageMaker Studio is to create a custom SageMaker docker image using the Scala kernel based on the Almond Scala Kernel SageMaker custom image sample and attach the image to the SageMaker domain. The option D is the correct answer.

Here's a detailed explanation for all the options:

Option A: Allow the developers to select the Scala kernel based on the Almond Scala Kernel from the list of included SageMaker images available in SageMaker studio.

This option is not the most efficient because it requires each developer to manually select the Scala kernel based on the Almond Scala Kernel from the list of included SageMaker images every time they create a new Jupyter notebook. This can be time-consuming, and there's a chance that developers might forget to select the correct kernel.

Option B: Create a custom SageMaker image based on an AMI that includes the Scala kernel based on the Almond Scala Kernel and attach the image to your SageMaker domain.

This option is better than option A, but it is still not the most efficient. Creating a custom SageMaker image based on an AMI that includes the Scala kernel based on the Almond Scala Kernel is a good way to ensure that all developers have the same environment, but it requires additional steps to create the image, and you need to manage the image separately.

Option C: Create a custom SageMaker docker image using the Scala kernel based on the Almond Scala Kernel SageMaker custom image sample and attach the image to each user's profile.

This option is also a valid option, but it is less efficient than option D. Creating a custom SageMaker docker image using the Scala kernel based on the Almond Scala Kernel SageMaker custom image sample and attaching the image to each user's profile requires additional steps for each user, and you need to manage the images separately for each user.

Option D: Create a custom SageMaker docker image using the Scala kernel based on the Almond Scala Kernel SageMaker custom image sample and attach the image to the SageMaker domain.

This option is the most efficient way to provide the required development environment to your developers for their Jupyter notebooks in SageMaker Studio. By creating a custom SageMaker docker image using the Scala kernel based on the Almond Scala Kernel SageMaker custom image sample and attaching the image to the SageMaker domain, you can ensure that all developers have the same environment, and you only need to manage one image for all users. This option also eliminates the need for developers to select the correct kernel every time they create a new Jupyter notebook.