Which of the following describes a data ingestion solution that utilizes Azure services?

The correct choice highlights a comprehensive approach to data ingestion by utilizing Azure services that seamlessly integrate into a modern data analytics workflow.

In the context of Azure services, this choice demonstrates the capabilities of Azure Synapse Analytics for data transformation. Extracting raw data and transforming it allows for the ingestion of data from various sources and preparing it for further analysis. This transformation step is crucial in data pipelines as it enhances the quality and usability of the data for model training.

Moreover, training models with Azure Machine Learning establishes a clear link between data engineering, where data is ingested and transformed, and data science, where actionable insights are derived from well-prepared datasets. Azure Machine Learning provides robust tools for developing, training, and deploying machine learning models, making it a fundamental component in a data ingestion solution that aims for analytics and model training.

This integrated workflow facilitated by Azure services supports the entire lifecycle of data, from ingestion through transformation to model training, highlighting the synergy of Azure's offerings in handling data science projects. Other options lack either the comprehensive integration of services or the focus on Azure-based solutions.