Exam DP-100 topic 2 question 106 discussion

Correct. The main difference between using the build and image parameters when defining an environment using the Azure Machine Learning Python SDK v2 is whether you want to define the environment from a Dockerfile or a Docker image. If you want to define an environment from a Dockerfile, you should use the build parameter and set it to a dictionary that specifies the contents of the Dockerfile using the dockerfile key. If you want to define an environment from a Docker image, you should use the image parameter and set it to the name of the Docker image you want to use. SaM

The correct answer is: B. image When defining an environment from a Docker image using the Azure Machine Learning Python SDK v2, the image parameter should be used. This parameter specifies the Docker image that will be used as the base environment for running the experiment.

The answer is correct. define from docker image using image parameter. https://learn.microsoft.com/en-us/azure/machine-learning/how-to-manage-environments-v2?view=azureml-api-2&tabs=python

image is correct. Reference: https://learn.microsoft.com/en-us/azure/machine-learning/how-to-manage-environments-v2?view=azureml-api-2&tabs=python

The Correct answer is: C. build Justification: When defining an environment from a Docker image using the Azure Machine Learning Python SDK v2, the appropriate parameter to use is build. This is because in the context of the Azure Machine Learning environment, the build parameter is used to specify the configuration for building the environment, including specifying the Docker image to be used. C. build: This is the correct parameter. Within the Environment class in SDK v2, the build parameter is used to define the build context, which includes specifying the Docker image. When creating an environment from a Docker image, you typically use the BuildContext class within build to specify the Docker image.

Correct. env_docker_image = Environment( image="pytorch/pytorch:latest", name="docker-image-example", description="Environment created from a Docker image.", ) ml_client.environments.create_or_update(env_docker_image)