easyMultiple Choice
Generative AI Leader Practice Question: Which Google service provides free access to…
Which Google service provides free access to Jupyter notebooks with GPU support for prototyping ML models?
⚠ Common exam trap
The trap is confusing Colab with Vertex AI Workbench or Kaggle Notebooks — the exam tests that Colab is the free, GPU-enabled Jupyter environment from Google, while Workbench is the paid enterprise option.
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
✓
Colab
Google Colab is a free, hosted Jupyter notebook environment that provides access to GPUs (and TPUs) for prototyping machine learning models without any setup. It integrates with Google Drive and allows users to write and run Python code in the browser. This directly matches the requirement for free Jupyter notebooks with GPU support.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Colab
Why this is correct
Colab supplies hosted Jupyter notebooks with free GPU runtimes, letting developers prototype ML models without provisioning infrastructure. It directly satisfies the stem's requirement for free notebook access with GPU support, unlike paid or non-notebook alternatives.
- ✗
Kaggle Notebooks
Why it's wrong here
Kaggle Notebooks do supply free Jupyter notebooks with GPU and TPU accelerators, so this option actually satisfies the scenario rather than failing it. It is tempting precisely because it is the correct choice; the distractor logic lies in confusing it with Colab, which offers the same free GPU notebook model under Google's branding.
- ✗
Vertex AI Workbench
Why it's wrong here
Vertex AI Workbench provides managed JupyterLab instances, but GPU-backed runtimes incur Vertex AI compute charges rather than free access. It is tempting because it is Google's flagship notebook environment for ML prototyping, and would be correct when the requirement is enterprise integration with Vertex AI pipelines, not zero-cost GPU access.
- ✗
BigQuery Studio
Why it's wrong here
BigQuery Studio runs SQL and notebook cells against BigQuery datasets, but its notebook runtime is serverless and billed through BigQuery slots rather than offering a free GPU-backed Jupyter environment. It is tempting because it does host notebooks, yet it targets warehouse analytics, not free GPU prototyping.
Go deeper
Related to this question
About these practice questions
One of 1,008 original Generative AI Leader practice questions on Courseiva, each with a full explanation and wrong-answer analysis — not exam dumps or protected exam content. Learn why practice questions differ from exam dumps →
JA
Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Google Cloud exam blueprint
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.