WBF Academy
Cloud Native Mastery

Cloud Native Mastery

Intermediate

Master containers, Kubernetes, and Infrastructure as Code from first principles, then deploy production agentic AI workloads to AWS, Azure, and GCP. Covers Docker, K8s primitives, Terraform, EKS/AKS/GKE, and the patterns for shipping LLM agents at scale.

📋 5 tracks ❓ 350 questions 💡 35 tips 🎬 15 videos ⏱ ~14h
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Course content

What Is a Container, Really 7:12

What Is a Container, Really

⏱ 7:12

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Building Container Images 7:40

Building Container Images

⏱ 7:40

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Registries and Image Distribution 8:00

Registries and Image Distribution

⏱ 8:00

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Practice questions

0/70 answered

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Kubernetes Workloads 7:03

Kubernetes Workloads

⏱ 7:03

▶
Networking and Configuration 7:26

Networking and Configuration

⏱ 7:26

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Control Plane Internals 7:51

Control Plane Internals

⏱ 7:51

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Practice questions

0/70 answered

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Terraform Fundamentals 6:53

Terraform Fundamentals

⏱ 6:53

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State and Collaboration 7:30

State and Collaboration

⏱ 7:30

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Modules, Patterns, and OpenTofu 7:32

Modules, Patterns, and OpenTofu

⏱ 7:32

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Practice questions

0/70 answered

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Amazon EKS in Production 8:07

Amazon EKS in Production

⏱ 8:07

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Azure AKS in Production 7:55

Azure AKS in Production

⏱ 7:55

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Google GKE in Production 8:26

Google GKE in Production

⏱ 8:26

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Practice questions

0/70 answered

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Serving LLM Agents on Kubernetes 8:18

Serving LLM Agents on Kubernetes

⏱ 8:18

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Autoscaling AI Workloads 8:23

Autoscaling AI Workloads

⏱ 8:23

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Cloud-Native AI Integrations 9:33

Cloud-Native AI Integrations

⏱ 9:33

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🧠

Practice questions

0/70 answered

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Certification Exam

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Certification Exam

Cloud Native Mastery

❓ 40
Questions
⏱ 120m
Time Limit
% 75%
To Pass

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❓ Answer 80% of the practice questions 0% / 80%

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Certification Exam

Cloud Native Mastery

#

40 Questions

All difficulty levels

⏱

120 Minutes

Auto-submits when time expires

%

75% to Pass

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Tips

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Observability Advanced

Observability for LLMs: track tokens

Latency and errors are not enough
AWS Intermediate

Call Bedrock Claude via IRSA, no keys

Pod identity > long-lived API tokens
AI-Workloads Advanced

Prompt cache sidecar in front of the model

Hash the prompt, skip the GPU
AI-Workloads Intermediate

Warm models before flipping ready

Cold-start latency is a real outage