You are developing a recommendation engine for an online clothing store. The historical customer transaction data is stored in BigQuery and Cloud Storage. You need to perform exploratory data analysis

User-managed notebooks require more administrative effort, such as VM management and environment setup, compared to managed notebooks, which increases development effort.

A Vertex AI Workbench managed notebook provides a fully managed, serverless environment, minimizing operational overhead and development effort for iterative EDA, preprocessing, and model training. These notebooks can directly browse and query data from BigQuery and Cloud Storage, offering an efficient way to interact with the datasets.

While Dataproc can handle large-scale data processing, setting up and managing a Dataproc Hub adds significant complexity, management overhead, and potentially higher costs compared to using a managed notebook for direct BigQuery interaction, which goes against the goal of minimizing effort and cost.

Configuring a Dataproc cluster and using Spark adds significant complexity, management overhead, and potentially higher costs if Spark processing is not strictly necessary, which contradicts the objective of minimizing cost and development effort.