You manage an Azure Machine Learning workspace.

Azure Machine Learning environment versions are effectively immutable with respect to their runtime definition . Microsoft documents that after an environment version has been registered, properties such as the Docker image, build context, and Conda dependencies cannot be modified. Only metadata properties such as the description and tags can be updated. Therefore, when a newer Docker image must be used, the correct approach is to define an Environment object representing a new environment version and register it with Azure Machine Learning.

For example, the new environment version can retain the existing environment name while specifying the updated image URI/tag and a new version. Registering an environment with the same name creates another version, preserving reproducibility for jobs that depend on the previous version.

Option A changes only descriptive metadata and has no effect on the execution image. Option B is incorrect because conda_file defines Python/Conda package dependencies rather than the Docker image version. Option C incorrectly implies that an existing registered environment ' s image tag can be modified through create_or_update; Microsoft explicitly states that the image is immutable after registration.

Study Guide Reference: Implement machine learning model lifecycle and operations — Azure Machine Learning environments, versioned assets, reproducible training environments, and SDK v2 asset management.