Cloud Digital Leader Fundamental Cloud Concepts Practice Question
A data scientist wants to train a machine learning model using a managed service that supports custom TensorFlow code and provides GPU/TPU hardware acceleration. They prefer not to manage the underlying infrastructure. Which Google Cloud service should they use?
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
✓
AI Platform Training (Vertex AI Training)
AI Platform Training (now Vertex AI Training) is a managed service that allows you to run custom TensorFlow/PyTorch training jobs on pre-configured VM instances with GPUs/TPUs, without managing the cluster.
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 pre-installed TensorFlow
Why it's wrong here
While Compute Engine allows installing TensorFlow on a custom VM, this approach requires manual setup of the environment, including drivers, dependencies, and ongoing OS patches. You also need to build your own distributed training infrastructure, manage scaling, and handle job failures. This operational overhead makes it a poor fit for a data scientist who wants a fully managed training experience.
- ✓
AI Platform Training (Vertex AI Training)
Why this is correct
Vertex AI Training is a fully managed service designed specifically for custom ML training, handling infrastructure provisioning, job scheduling, and scaling automatically. It supports hardware accelerators like GPUs and TPUs, and provides pre-built containers for TensorFlow, PyTorch, and scikit-learn. This lets the data scientist focus on model code rather than cluster management, making it the correct choice.
- ✗
Cloud Functions
Why it's wrong here
Cloud Functions is a serverless compute platform for lightweight, event-triggered functions with strict timeouts (typically 9 minutes) and no support for GPUs or large memory allocations. Training a complex ML model often requires hours of compute and distributed resources, which exceeds these limits. Thus it cannot handle training workloads and is unsuitable for this use case.
- ✗
Google Kubernetes Engine with GPU nodes
Why it's wrong here
Google Kubernetes Engine with GPU nodes offers flexible and scalable training by allowing you to run containers on a custom-managed cluster. However, this requires you to set up, operate, and maintain the Kubernetes cluster, including node pools, GPU drivers, autoscaling, and workload scheduling. Since the user explicitly wants to avoid infrastructure management, GKE introduces unnecessary operational burden compared to a managed training service.
Go deeper
Related to this question
Learn chapter
Cloud Digital Transformation
Key term
Service
A service is a software component or system that performs a specific function and is available to be used by other programs or users over a network.
Key term
Model
In IT and AI, a model is a trained mathematical representation that learns patterns from data to make predictions or decisions.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This GCDL 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 GCDL exam.