When using env.yml with conda whats the difference between dependencies and pip dependencies?

I am creating a .SH script for setting up automatically my dev environment in Azure ML, according to this:

https://learn.microsoft.com/en-gb/azure/machine-learning/how-to-customize-compute-instance

The script looks like this:

#!/bin/bash
 
set -e
# https://pypi.org/project/azure-ai-ml/ 
# Requires: Python <4.0, >=3.7
# This script creates a custom conda environment and kernel based on a sample yml file.

conda env create  python=3.10
#conda env create -f env.yml

echo "Activating new conda environment"
conda activate envname
conda install -y ipykernel
echo "Installing kernel"
sudo -u azureuser -i <<'EOF'
conda activate envname
python -m ipykernel install --user --name envname --display-name "mykernelp310v2"
echo "Conda environment setup successfully."
pip install azure-ai-ml
EOF

my env looks like this:

name: p310v2

dependencies:
  - python=3.10
  - numpy
  - matplotlib
  - pandas
  - scikit-learn
  - pip:
       -kaggle==1.5

When I check this document:

https://carpentries-incubator.github.io/introduction-to-conda-for-data-scientists/04-sharing-environments/index.html

I am confused between the dependencies section and the pip section. For example scikit-learn I could put in dependencies but also on the pip section, so whats the deal here?