Which of the following is a key feature of the Databricks Runtime for Machine Learning?

The choice highlighting the pre-installed popular ML libraries such as TensorFlow, PyTorch, and Scikit-learn is significant because it reflects the goal of the Databricks Runtime for Machine Learning to streamline the data science workflow. This runtime environment is specifically optimized for machine learning tasks, ensuring that practitioners have immediate access to widely used tools without the overhead of individual installations and configurations.

Having these libraries pre-installed allows data scientists and machine learning engineers to quickly start building, training, and deploying models. It enhances productivity as users can focus on developing their algorithms and experiments rather than worrying about setting up their machine learning environment. The convenience of a ready-to-use environment also promotes consistency across teams, ensuring that everyone is working with the same set of tools and libraries, fostering collaboration and reducing compatibility issues.

While integrated support for SQL queries, real-time data ingestion capabilities, and on-demand data cleaning tools are valuable features in various contexts, they do not specifically define the core strength of the Databricks Runtime for Machine Learning, which is aimed at providing an efficient and effective foundation for machine learning development and deployment.