What is Vertex AI Academy?

Vertex AI Academy is the #1 free interactive learning platform by Infinity Tech Stack for Google Cloud Vertex AI and the Gemini model family. It covers Gemini 3.8 Flash, Gemini 3.5 Flash-Lite, Context Caching, Grounding with Google Search, Managed Agents, and Remote MCP across 13 modules, 33 lessons, and 54+ quizzes with hands-on gcloud labs and zero sign-up required.

How is Infinity Vertex AI Academy different from Google Cloud Skills Boost?

Unlike Google Cloud Skills Boost which requires GCP cloud credentials, project setup, and credits, Infinity Tech Stack's Vertex AI Academy is 100% free with in-browser terminal simulations covering Gemini 3.8 Flash, Context Caching, Google Search Grounding, BigQuery ML, and Google ADK with no sign-up or credit card required.

How long does Vertex AI Academy take to complete?

Vertex AI Academy contains approximately 2 to 3 hours of hands-on interactive training across 13 modules and 33 lessons. Progress is saved locally in your browser so you can learn at your own pace.

Is the Vertex AI Academy free?

Yes, it is 100% free with no sign-up required. All 13 modules, lessons, and quizzes are immediately accessible. Progress is saved locally in your browser.

What topics does the Vertex AI course cover?

The course covers the Vertex AI platform, Gemini 3.8 Flash, Gemini 3.5 Flash-Lite, Gemini 2.5 Pro, Deep Think reasoning, 1M+ token Context Windows, Context Caching, BigQuery ML, Grounding with Google Search, Vertex AI Studio, and Agent Builder.

What is Context Caching in Vertex AI?

Context Caching lets you store large context payloads (documents, system prompts) server-side and reuse them across multiple requests, reducing latency and cost by up to 75% for repetitive workloads.

What is Grounding with Google Search?

Grounding connects Gemini to live Google Search results, enabling the model to provide up-to-date, factual responses with source citations - reducing hallucinations in production applications.

Do I need a Google Cloud account?

No account is needed to take the course. The built-in browser labs simulate the gcloud CLI so you can practice without a GCP account; if you want to follow along for real, a GCP free tier account is sufficient for most exercises.

Does the Vertex AI Academy include hands-on labs?

Yes. Hands-on terminal labs are built into the lessons - authenticate with gcloud, enable the Vertex AI API, call Gemini via REST, and run Google's Agent Development Kit using real, vendor-verified commands, with objectives, hints, solutions, and XP rewards. No sign-up needed.

What is Deep Think in Gemini?

Deep Think is Gemini's extended reasoning mode that allocates additional compute for complex multi-step problems - similar to chain-of-thought reasoning - producing more accurate results for math, coding, and scientific analysis.

Which Google Cloud IAM role grants permission to execute Gemini model inference without administrative project access?

roles/aiplatform.user — roles/aiplatform.user provides the precise permissions needed to invoke model predictions and run inference.

How do VPC Service Controls (VPC-SC) protect enterprise AI workloads on Vertex AI?

They establish cryptographic security perimeters that block unauthorized data exfiltration across network boundaries — VPC Service Controls isolate Google Cloud services within secure perimeters, mitigating insider threats and preventing unauthorized data exfiltration.

What is the primary compliance benefit of Vertex AI Data Residency controls?

It confines prompt processing, embeddings, and data storage to explicitly chosen GCP geographic regions (e.g., GDPR europe-west4) — Data Residency guarantees that customer payloads and inference execution remain strictly confined to designated sovereign jurisdictions.

Enterprise Security & IAM Tutorial | The Vertex AI Ecosystem

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Enterprise Security & IAM

☁️ The Vertex AI Ecosystem 15m 200 BASE XP⌨ HANDS-ON LAB

Enterprise Security Architecture on Google Cloud

Unlike consumer AI offerings, Vertex AI operates strictly within your Google Cloud enterprise security boundary. It natively integrates with Cloud IAM, VPC Service Controls (VPC-SC), Cloud KMS (Customer-Managed Encryption Keys), and Cloud Audit Logs.

Zero Training on Customer Data (ZDR)

When you interact with foundation models (such as Gemini 3.8 Flash or Gemini 2.5 Pro) through Vertex AI, Google provides a legally binding contractual guarantee: your prompts, inputs, and generated completions are never used to train Google foundational models, nor are they exposed to other customers.

Identity & Access Management (IAM) Roles

Granular IAM permissions allow administrators to enforce the principle of least privilege across machine learning projects:

roles/aiplatform.user: Authorizes invoking deployed online prediction endpoints, running inference against Gemini models, and querying feature stores.
roles/aiplatform.viewer: Read-only access to inspect pipelines, model registry artifacts, metadata, and dataset schemas without making changes or running inference.
roles/aiplatform.admin: Full administrative privileges to deploy custom models, configure VPC service perimeters, allocate endpoints, and adjust quotas.

VPC Service Controls & Data Residency

By placing your Vertex AI resources inside a VPC Service Controls security perimeter, you mathematically prevent data exfiltration. Vertex AI endpoints cannot communicate with untrusted internet destinations. Furthermore, Vertex AI supports strict Data Residency: you pin model execution and prompt processing to specific Google Cloud regions (e.g., europe-west4 in Eemshaven, Netherlands or us-central1 in Iowa), ensuring strict compliance with GDPR, HIPAA, and regional sovereign AI regulations.

# Grant minimum inference permissions to a microservice service account
gcloud projects add-iam-policy-binding PROJECT_ID \
    --member="serviceAccount:ai-service-runner@PROJECT_ID.iam.gserviceaccount.com" \
    --role="roles/aiplatform.user"

# Enforce VPC-SC ingress/egress perimeter around Vertex AI
gcloud access-context-manager perimeters update SECURE_AI_PERIMETER \
    --add-restricted-services="aiplatform.googleapis.com"

⌨ HANDS-ON LABConfigure Vertex AI IAM Roles & Service Account

⭐ +150 XP

Assign fine-grained enterprise IAM roles to a Vertex AI service account ensuring least-privilege access for automated training and model inference.

1Assign the Vertex AI User role to the service account using gcloud projects add-iam-policy-binding.

lab-sandbox — simulated environment

INFINITY LAB SANDBOX v2.6 — simulated shell
Type the command for the current objective. Helpers: "hint", "solution", "clear".
$

OBJECTIVE 1 / 1 — type "hint" if stuck

SYNAPSE VERIFICATION

QUERY 1 // 4

What contractual guarantee does Google Cloud provide regarding customer prompt data in Vertex AI?

Prompts are added to the public training pool after 90 days

Customer data and completions are never used to train Google foundational models

Prompts are publicly indexed in the Model Garden

Only image inputs are stored permanently