Cloud · Google Cloud
Vertex AI — Free Learning Resources
Free, printable resources for Vertex AI — practice problems, quick-reference cheatsheet, and an interview prep sheet. No sign-up required.
Vertex AI — Practice Worksheet
Structured exercises and problems to build hands-on Vertex AI skills. Work through key concepts step by step.
Vertex AI — Cheatsheet
One-page quick-reference for Vertex AI — key syntax, commands, patterns, and best practices at a glance.
Vertex AI — Interview Sheet
Top Vertex AI interview questions with concise answers. Get ready for any technical round with this focused prep sheet.
About Vertex AI
Vertex AI is a cloud computing platform that provides on-demand infrastructure, platform services, and managed products. It enables teams to provision resources in minutes, scale automatically, and pay only for usage — without managing physical hardware.
Vertex AICheat Sheet — What's Covered
- ✓Vertex AI core services — compute, storage, networking, and managed databases
- ✓IAM — identities, roles, policies, and least-privilege access
- ✓CLI and SDK usage for automation and infrastructure as code
- ✓Cost optimization — pricing model, reserved capacity, and tagging
- ✓Security, compliance, and monitoring fundamentals
Frequently Asked Questions — Vertex AI
What is Vertex AI and what problems does it solve?
Vertex AI provides on-demand computing resources. Instead of buying servers, teams provision VMs, databases, and storage in minutes, scale automatically under load, and pay only for actual usage — eliminating capital expenditure on infrastructure.
How does Vertex AI handle identity and access management?
Vertex AI uses IAM to control who can access which resources. Define users, groups, and roles with attached policies that grant specific permissions. Follow the principle of least privilege — grant only the permissions needed for each identity's job.
How do you achieve high availability on Vertex AI?
Deploy across multiple availability zones (physically separate data centers in the same region). Use load balancers to distribute traffic. Configure auto-scaling to replace failed instances. Multi-region active-active deployment provides the highest availability.
How do you manage costs on Vertex AI?
Tag all resources by team and project. Use reserved instances or committed use discounts for predictable workloads (up to 60% cheaper than on-demand). Set billing alerts. Review the cost explorer weekly. Right-size instances — over-provisioning is the most common waste.
What monitoring capabilities does Vertex AI provide?
Most cloud platforms include built-in metrics dashboards, log aggregation, and alerting. Enable platform-native monitoring for resource metrics and set alerts for CPU, memory, error rates, and latency. Add distributed tracing for microservices to diagnose latency across service boundaries.
Who Is This For?
DevOps engineers, cloud architects, and developers who deploy and manage workloads on Vertex AI and need a quick command and concept reference.