Cloud Digital Leader Why Cloud Technology Can Transform Business 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.

• ✓

  AutoML Vision

  Why this is correct

  AutoML Vision is the correct choice because it provides a fully managed pipeline for training a custom image classification model on your own labeled dataset. You simply upload images and specify labels; the service automatically handles data preprocessing, architecture search, hyperparameter tuning, and distributed training without requiring you to provision or operate any compute infrastructure. The trained model is then deployed behind a scalable API endpoint, ensuring minimal infrastructure management and fast time-to-deployment.

• ✗

  TensorFlow on Compute Engine

  Why it's wrong here

  TensorFlow on Compute Engine is wrong because it requires your team to manage the entire infrastructure lifecycle. You must provision and maintain VMs, install TensorFlow and CUDA drivers, configure GPU accelerators, implement distributed training logic, and manually monitor job restarts and scaling. While this gives maximum low-level control and flexibility, it contradicts the stated goal of minimizing infrastructure management and demands significant ML engineering expertise.

• ✗

  Cloud Vision API

  Why it's wrong here

  The Cloud Vision API is wrong because it offers only pre-trained models that perform generic tasks such as label detection, OCR, and safe-search filtering; it cannot be trained or fine-tuned on your own labeled images. The model weights are fixed and shared across all customers, so the API cannot learn your domain-specific categories or custom labels. It is a useful prediction-time tool or a starting point for transfer learning, but it does not provide any capability for custom model training.

• ✗

  Vertex AI Workbench

  Why it's wrong here

  Vertex AI Workbench is wrong because it is an interactive, notebook-based development environment intended for data scientists who write and run custom training code. Each Workbench instance runs on a Compute Engine VM that you must select, size, and manage, meaning you are still responsible for infrastructure operations. Unlike AutoML's automated training pipeline, Workbench does not offer a low-code or no-code model training workflow; it is a tool for building custom code, not a managed alternative for hands-off training.

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