Cloud Digital Leader Google Cloud Products and Services Practice Question

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

• ✗

  BigQuery ML

  Why it's wrong here

  BigQuery ML enables SQL-based model building for linear regression, binary classification, and other simple algorithms, with training executed on BigQuery's shared infrastructure. However, it does not allow custom neural network architectures or fine-grained control over distributed training, as it abstracts away the underlying cluster. For a bespoke ML model requiring custom layers or multi-node training, BigQuery ML is too constrained.

• ✓

  Vertex AI Training

  Why this is correct

  Vertex AI Training is a fully managed service for custom model training that provisions and orchestrates compute clusters on demand. It natively supports distributed training by splitting data/model across multiple workers and accelerators, and it automates hyperparameter tuning and experiment tracking. This makes it the appropriate service for a data scientist to run a custom ML model that needs to scale beyond a single machine.

• ✗

  Cloud Functions

  Why it's wrong here

  Cloud Functions is an event-driven, serverless compute platform designed for short-lived, single-purpose functions, with generous limits but a maximum runtime of 9 minutes (gen1) or 60 minutes (gen2) and limited GPU support. Custom ML training, especially deep learning, often runs for hours and requires resource-intensive, stateful computation that exceeds these constraints. Thus Cloud Functions is not a practical choice for building or training a large model.

• ✓

  Vertex AI Workbench

  Why this is correct

  Vertex AI Workbench provides a Jupyter-based notebook environment where data scientists can interactively explore data, prototype code, and visually inspect results. It is built for development and experimentation, and it integrates with Vertex AI Training so that a notebook can directly submit a custom training job to a scaled-out cluster. This makes Workbench the correct starting point for developing a custom model, though the actual training runs on Vertex AI Training.

• ✗

  Cloud Run

  Why it's wrong here

  Cloud Run is a managed serverless container platform that executes stateless HTTP requests, scaling containers from zero and billing by the number of invocation requests. While it can run any binary, it is not designed for long-running, resource-intensive training jobs because it restricts concurrency, has a maximum request timeout, and does not provide the distributed coordination required for multi-machine training. Its typical use case is serving a trained model or running lightweight preprocessing, not model training.

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