PDE Preparing and Using Data for Analysis Practice Question
A data scientist wants to train a custom TensorFlow model on Vertex AI using a managed Jupyter notebook. Which Vertex AI service should they use to set up a notebook environment with pre-installed deep learning frameworks?
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Vertex AI Workbench
Vertex AI Workbench provides managed Jupyter notebooks with pre-installed deep learning frameworks (TensorFlow, PyTorch, etc.) and easy scaling options. Notebooks on Compute Engine would require manual setup. AI Platform Training is for training jobs, not interactive notebooks. Vertex AI Pipelines is for orchestrating ML workflows.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Compute Engine with Deep Learning VM
Why it's wrong here
Deep Learning VM is a Compute Engine image, not a Vertex AI managed notebook service, so it fails the managed-notebook requirement. It tempts because it does ship pre-installed TensorFlow and Jupyter, and would be correct for self-managed training on raw Compute Engine instances.
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Vertex AI Training via custom job
Why it's wrong here
A custom training job runs packaged training code to completion on Vertex AI; it provides no interactive Jupyter notebook environment. It tempts because it is the Vertex AI service for custom TensorFlow training, and would be correct when submitting a training script rather than exploring data interactively.
- ✓
Vertex AI Workbench
Why this is correct
Vertex AI Workbench provides managed JupyterLab notebook instances with pre-installed deep learning frameworks such as TensorFlow and PyTorch, plus optional GPU accelerators. This satisfies the requirement for a managed notebook environment ready for custom TensorFlow training without manual framework installation.
- ✗
Vertex AI Pipelines
Why it's wrong here
Vertex AI Pipelines orchestrates ML workflows as containerised steps; it hosts no interactive notebook with pre-installed frameworks. It tempts because it is a core Vertex AI service for automating training, and would be correct when chaining repeatable training, evaluation and deployment steps.
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