easyMultiple ChoiceObjective-mapped
PMLE Practice Question: A marketing team wants to use a pre-built natural…
A marketing team wants to use a pre-built natural language processing (NLP) model from Vertex AI Model Garden to analyze customer feedback. They need to extract sentiment from text data stored in Cloud Storage. The team has no experience with model serving infrastructure. Which deployment option minimizes operational overhead?
⚠ Common exam trap
Candidates often assume Cloud Functions or Cloud Run are simpler because they are 'serverless,' but they fail to recognize that deploying a large NLP model requires specialized infrastructure (GPUs, model serving frameworks) that these services do not natively provide without significant custom work.
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
✓
Deploy the model to a Vertex AI Endpoint directly from Model Garden.
Deploying directly to a Vertex AI Endpoint from Model Garden eliminates all infrastructure management. Vertex AI handles model serving, scaling, and monitoring automatically, which is ideal for a team with no experience in model serving infrastructure. This is a fully managed, serverless deployment that requires no containerization or server configuration.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Deploy the model as a Cloud Function invoked by Cloud Storage events.
Why it's wrong here
Not designed for low-latency inference with autoscaling.
- ✗
Deploy the model as a Cloud Run service using a custom Docker container.
Why it's wrong here
Requires containerization and more configuration.
- ✗
Deploy the model on App Engine flexible environment.
Why it's wrong here
App Engine flexible environment requires the team to manage scaling configurations and runtime dependencies, directly contradicting the stem’s requirement to minimise operational overhead for a team with no model serving infrastructure experience. It is tempting because App Engine is a fully managed platform for web applications, and would be correct if the team needed to deploy a custom web service with flexible runtime customisation rather than a pre-built NLP model from Model Garden.
- ✓
Deploy the model to a Vertex AI Endpoint directly from Model Garden.
Why this is correct
Simplest deployment with managed infrastructure.
Quick reference
Cloud Service Model Comparison
| Model | You Manage | Provider Manages | Examples |
|---|---|---|---|
| IaaS | OS, runtime, apps, data | Hardware, hypervisor, networking | EC2, Azure VMs, GCP Compute Engine |
| PaaS | Apps and data | OS, runtime, middleware, hardware | Elastic Beanstalk, Azure App Service |
| SaaS | Data and settings only | Everything else | Microsoft 365, Salesforce, Workday |
| FaaS / Serverless | Function code only | Infra, scaling, runtime | Lambda, Azure Functions, Cloud Run |
| CaaS | Containers and apps | Kubernetes, OS, hardware | EKS, AKS, GKE |
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This PMLE 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 PMLE exam.