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title: “AWS Certification Training Workflow” created: 2026-07-20 updated: 2026-07-20 type: knowledge tags:

  • aws
  • certification
  • workflow
  • zettelkasten
  • methodology

AWS Certification Training Workflow

What It Is

A repeatable process for turning AWS training lab content (HTML from the training platform) into structured, interlinked knowledge notes organized in a zettelkasten-style system. The workflow leverages Kiro as an AI assistant to accelerate extraction and structuring.

Why It Matters

Raw training materials are hard to review and retain. By extracting atomic concepts and filing them by domain, you build a personal knowledge graph that supports spaced retrieval, cross-linking, and exam-ready review.

Key Concepts

The Three-Phase Approach

PhaseInputOutputWhere
1. CaptureHTML from training platformClean Markdown fileaws-certification/training/
2. Extract & DispatchMarkdown training fileAtomic zettelkasten notesaws-certification/learnings/<domain>/
3. MOC (Map of Content)Atomic notes from phase 2Topic index pageaws-certification/learnings/<topic>-moc.md

Zettelkasten Domains

Notes are dispatched into domain folders based on their primary topic:

  • compute/ — EC2, Lambda, ECS, EKS, Fargate
  • storage/ — S3, EBS, EFS, FSx
  • networking/ — VPC, Route53, CloudFront, ELB
  • databases/ — RDS, DynamoDB, Aurora, ElastiCache
  • security/ — IAM, KMS, WAF, Shield, ACLs, policies
  • monitoring/ — CloudWatch, CloudTrail, X-Ray
  • architecture/ — Well-Architected, patterns, DR
  • serverless/ — Lambda patterns, API Gateway, Step Functions
  • devops/ — CI/CD, CLI, SSM, IaC

Note Principles (Zettelkasten)

  1. One idea per note — each file captures a single concept or service
  2. Interlink heavily — use [[wiki-links]] to connect related ideas
  3. Use the knowledge template — consistent frontmatter with type: knowledge
  4. Source attribution — note which training the knowledge came from
  5. Grow organically — add notes as you learn, refine over time

How It Works

Phase 1: Structure Setup (one-time)

Create the folder hierarchy under aws-certification/:

  • training/ — raw training materials (HTML + converted MD)
  • learnings/ — zettelkasten with domain sub-folders, each with a README index

Phase 2: Training Capture

  1. Copy HTML from the AWS training platform
  2. Paste into training/<Training-Name>.html
  3. Ask Kiro to convert to Markdown (same name, .md suffix)
  4. The MD file preserves all content: objectives, steps, code blocks, policies

Phase 3: Analysis & Dispatch

  1. Kiro reads the MD training file
  2. Identifies distinct learnable topics (concepts, services, patterns)
  3. Groups them by domain (storage, security, devops, etc.)
  4. Creates one atomic note per topic using the knowledge template
  5. Interlinks notes with [[wiki-links]]
  6. Updates domain README indexes with new entries

Phase 4: MOC (Map of Content)

After dispatching notes, create or update a topic-level index page:

  1. Check if learnings/<topic>-moc.md already exists (e.g. s3-moc.md)
  2. If not, create it with type: moc in frontmatter
  3. Link to the training source (the lab in training/)
  4. Group all related atomic notes by domain (storage, security, devops…)
  5. Add review questions to support self-testing
  6. If the MOC already exists, enrich it with the new notes from this training

Why MOCs matter:

  • They provide a single entry point per topic across all domains
  • They work everywhere: Obsidian (graph hub), mdbook, publish, GitHub
  • Unlike tags, they are navigable pages — not just search filters
  • They enable topic-based revision independent of training sequence

Naming convention: <service-or-topic>-moc.md — e.g. s3-moc.md, ec2-moc.md, iam-moc.md

Examples

From the “Introduction to Amazon S3” training, 11 atomic notes were extracted:

  • Storage (4): s3-fundamentals, s3-bucket-configuration, s3-object-operations, s3-versioning
  • Security (5): s3-block-public-access, s3-access-control-lists, s3-bucket-policies, aws-arns, iam-instance-profiles
  • DevOps (2): aws-cli-s3-commands, ssm-session-manager

Then a MOC was created: [[learnings/s3-moc|S3 — Map of Content]] linking all 11 notes + training source + review questions.

Gotchas

  • Some topics cross multiple domains — place in the primary domain and link from others
  • Training labs are procedural; extract the concepts behind the procedures, not the step numbers
  • Don’t duplicate knowledge already captured — check existing notes first and enrich them instead
  • HTML formatting varies — Kiro handles the cleanup, but verify code blocks and JSON are intact

See Also

  • [[training/README]] — Training materials index
  • [[learnings/README]] — Zettelkasten index
  • [[learnings/s3-moc]] — S3 Map of Content (example MOC)