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Generative AI Leader Practice Question: A data analyst wants to experiment with ML models…
A data analyst wants to experiment with ML models using a free, cloud-based Jupyter notebook environment with GPU support. Which Google tool should they use?
⚠ Common exam trap
Many exam-takers confuse Vertex AI Workbench (a paid, enterprise tool) with a free offering because both provide Jupyter notebooks with GPU support, but the question's requirement for a 'free' tool eliminates it.
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
✓
Google Colab
Google Colab is a free, cloud-based Jupyter notebook environment that provides access to GPUs (e.g., NVIDIA T4 or V100) without requiring any setup or billing. It is specifically designed for experimentation and learning with ML models, making it the correct choice for a data analyst seeking a free, GPU-enabled notebook environment.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Cloud Shell
Why it's wrong here
Cloud Shell is a free browser terminal with gcloud access and persistent storage, but it provides no GPU and is not a Jupyter notebook environment. Colab is the correct tool. Cloud Shell suits running CLI commands and scripts for managing Google Cloud resources, not training ML models.
- ✓
Google Colab
Why this is correct
Google Colab provides a free, cloud-hosted Jupyter notebook environment with optional GPU runtimes, requiring no local setup. This matches the analyst's constraints of free access, cloud-based notebooks and GPU support, unlike Vertex AI workbench or plain BigQuery.
- ✗
BigQuery Studio
Why it's wrong here
BigQuery Studio provides SQL and notebook analysis over BigQuery data but does not offer free GPU-backed compute for training models. Colab is the correct tool. BigQuery Studio suits querying and exploring warehouse datasets at scale, not running GPU-accelerated ML experiments in a notebook.
- ✗
Vertex AI Workbench
Why it's wrong here
Vertex AI Workbench offers a managed, cloud-based Jupyter notebook environment with GPU acceleration, making it a powerful platform for professional machine learning development within Google Cloud. However, it is a paid service, incurring charges for compute, storage, and other resources, which directly contradicts the question's requirement for a *free* environment. This option would be correct if the data analyst sought a fully integrated, scalable, and secure development platform for enterprise-grade ML projects where cost is not the primary constraint.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This Generative AI Leader practice question is part of Courseiva's free Google Cloud certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the Generative AI Leader exam.