Note: This question is part of a series of questions that present the same scenario.

No. Removing the existing Python 3.6 - AzureML kernel is not a prerequisite for adding a new kernel. Azure Machine Learning supports multiple installed Jupyter kernels on the same compute instance, and notebooks can select among those kernels without requiring one existing kernel to be removed first. Microsoft states that the notebook environment automatically discovers all Jupyter kernels installed on the connected compute instance.

The supported new-kernel workflow begins by creating a separate environment, for example with conda create --name newenv, and activating it. The required pip and ipykernel packages are then installed in the environment before registering it with Jupyter by using python -m ipykernel install --user --name newenv.

Deleting an existing AzureML kernel provides no technical benefit to that workflow. Microsoft also explicitly cautions users not to delete Conda environments or Jupyter kernels they did not create , because doing so can interfere with built-in Jupyter/JupyterLab functionality on the compute instance.

Therefore, the existing Python 3.6 kernel should remain untouched; the new kernel should be installed alongside it in its own environment.

Study Guide Reference: Design and implement an MLOps infrastructure — Jupyter kernel management, Conda environments, Azure Machine Learning compute instances, and development environment isolation.