Cloud Digital Leader Google Cloud Products and Services Practice Question
A team is developing a machine learning model using TensorFlow. They want to train the model on a large dataset stored in Cloud Storage, using GPUs, and then deploy the trained model for online predictions with autoscaling. Which GCP service should they use for the entire workflow?
⚠ Common exam trap
The trap is confusing Vertex AI with legacy AI Platform or assuming that raw Compute Engine is sufficient; the exam tests that Vertex AI is the current, unified service for end-to-end ML workflows.
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
✓
Vertex AI
Vertex AI is Google Cloud's unified machine learning platform that supports the entire ML workflow: data ingestion from Cloud Storage, training with GPUs, model deployment for online predictions, and autoscaling. It integrates with TensorFlow and provides managed services for each stage. AI Platform (legacy) is deprecated and lacks the full unified experience.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Vertex AI
Why this is correct
Vertex AI provides a unified managed platform that covers the full ML lifecycle: training with custom or pre-built containers on GPU/TPU, versioning in a Model Registry, and deployment to prediction endpoints with built-in autoscaling based on traffic. It eliminates the need to manually configure infrastructure, allows custom model serving for any framework, and offers MLOps capabilities like monitoring and drift detection. This makes it the recommended service for training and serving TensorFlow models.
- ✗
AI Platform (legacy)
Why it's wrong here
AI Platform (legacy) originated as a separate ML platform and is now absorbed into Vertex AI as a legacy service. While it supports training and prediction, it lacks integration with Vertex AI's unified pipelines, feature store, and model monitoring, and Google is not investing in new features for it—only maintenance. The correct choice for new projects is Vertex AI because it provides a consistent interface for batch and online prediction, as well as a migration path from legacy AI Platform.
- ✗
Cloud Functions
Why it's wrong here
Cloud Functions is an event-driven, serverless compute service designed for short-lived, single-purpose functions (e.g., webhooks, lightweight data processing). It does not support GPUs, imposes strict execution timeouts (typically up to 540 seconds), and is not built for long-running ML training or continuous HTTP model serving with autoscaling. Consequently, it cannot handle TensorFlow training or production-grade online inference where request patterns are variable and require model-specific infrastructure.
- ✗
Compute Engine with pre-installed ML frameworks
Why it's wrong here
Compute Engine with pre-installed ML frameworks gives you raw VMs, which require you to manually manage GPU drivers, framework versions, and serving dependencies. To achieve autoscaling for prediction, you must build your own infrastructure: set up instance groups, health checks, load balancers, and metrics-based autoscaling policies—all of which Vertex AI provides out of the box. This operational overhead increases risk and maintenance burden, and it is not a purpose-built solution for the end-to-end ML workflow.
Go deeper
Related to this question
Learn chapter
ML Lifecycle: Data, Training, Deployment
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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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 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.