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Microsoft Sovereign Cloud Brain Trek

Level 200 Visual Asset Specifications

Purpose: Detailed design briefs for all 18 Level 200 visual assets (Assets 21–38) Created: October 21, 2025 Asset Registry: See docs/assets/images/README.md for short specs and usage

Module 1: Azure Local Architecture Deep Dive

Section titled “Module 1: Azure Local Architecture Deep Dive”

Asset 21: Advanced Networking Architecture

Section titled “Asset 21: Advanced Networking Architecture”

Context: This diagram helps architects and networking engineers design Azure Local clusters with resilient, high-performance network topologies. It explains SET (Switch Embedded Teaming), VLAN segmentation, and RDMA optimizations needed for low-latency, high-throughput storage and compute traffic.

Design Constraints:

  • Canvas 1400x1000 px, 50px margins
  • Use Microsoft color palette and Segoe-like typography
  • Maintain clarity at 800px width for inline displays
  • Compatible with accessibility requirements (patterns + colors)

Content Requirements:

  • Physical server with 6–8 NICs
  • Virtual switch (vSwitch) + SET team labels
  • VLAN segmentation: management, storage, cluster, customer
  • RDMA path overlay (separate line style)
  • Dual ToR switches and failover paths
  • Bandwidth allocation indicators (eg. 10Gb/25Gb/100Gb)
  • Legend and short notes

Visual Elements:

  • Server icon with adapter slots
  • vSwitch block showing teaming
  • Color-coded VLAN flows (blue, orange, green, purple)
  • Solid arrows for active, dashed for failover
  • Small iconography for redundancy and QoS

Wireframe Guidance: Top: server with ports; center-left: vSwitch visualization; center-right: ToR switches; bottom: RDMA overlay and storage array. Legend on the right.

Acceptance Criteria:

  • Shows 6–8 NICs labeled
  • SET team and vSwitch are clearly depicted
  • VLANs color-coded and legible
  • RDMA paths visually distinct and labeled
  • Dual-switch redundancy is evident
  • Bandwidth nodes present with numeric values
  • Legend explains colors and line styles
  • Alt text provided in final SVG

Microsoft Learn Adaptation:

Asset 22: High-Availability Architecture

Section titled “Asset 22: High-Availability Architecture”

Context: Shows HA patterns for Azure Local: node layout, quorum options, storage resilience, and failover. Useful for operations and DR planning.

Design Constraints:

  • Canvas 1300x900 px
  • Include options for 3-node and 4-node clusters
  • Keep diagrams simple for export to slides

Content Requirements:

  • Cluster node arrangement (3/4 nodes)
  • Storage Spaces Direct replication modes
  • Quorum options: disk, file share, cloud witness
  • Replication/backup flows and RTO/RPO annotations
  • Failover scenarios illustrated

Visual Elements:

  • Circular node layout with storage tier in center
  • Arrows for replication paths
  • Highlighted quorum options with callouts
  • Color states: active/standby/failed

Wireframe Guidance: Top left: 3-node cluster; top right: 4-node cluster; bottom: quorum options and storage replication details. Provide side column with RTO/RPO guidance.

Acceptance Criteria:

  • Shows both 3-node and 4-node topologies
  • Quorum placement options annotated
  • Storage redundancy types labeled (2-way/3-way/EC)
  • Failover path visible and explained
  • RTO/RPO indicators present
  • Diagram readable at 1024 width
  • Accessibility: color + pattern for states

Microsoft Learn Adaptation:

Asset 23: Hardware Planning Decision Tree

Section titled “Asset 23: Hardware Planning Decision Tree”

Context: A decision flowchart guiding hardware selection based on workload class, capacity, redundancy and budget.

Design Constraints:

  • Canvas 1200x1400 px (vertical)
  • Decision diamonds and endpoint recommendation cards

Content Requirements:

  • Start: workload requirements
  • Branches: performance tier, capacity, redundancy, environment
  • Endpoints: validated BOM recommendations

Visual Elements:

  • Diamonds for decisions, rectangles for actions
  • Color-coded outcome paths for recommended tiers
  • Cost/perf mini-metrics on endpoints

Wireframe Guidance: Top: start node. Follow vertical tree down with 3–5 layers. Endpoints at bottom with recommended configs.

Acceptance Criteria:

  • Flow covers performance, capacity, redundancy, budget
  • Endpoints include BOM and short rationale
  • No decision path exceeds 5 hops
  • Visuals use accessible colors and patterns
  • All endpoints have brief cost guidance

Microsoft Learn Adaptation:

Module 2: Arc Advanced Management

Section titled “Module 2: Arc Advanced Management”

Asset 24: Arc Governance Framework

Section titled “Asset 24: Arc Governance Framework”

Context: Explains layered governance across tenant, subscription, resource group and resource using Azure Policy and RBAC for Arc-managed resources.

Design Constraints:

  • Canvas 1300x900 px
  • Hierarchical/pyramid visual

Content Requirements:

  • Policy layers and inheritance arrows
  • RBAC role mapping examples
  • Monitoring feedback loops and remediation paths

Visual Elements:

  • Layered pyramid with arrows, small RBAC boxes, policy iconography
  • Compliance status indicators (green/red)

Wireframe Guidance: Left: pyramid showing layers; center: enforcement arrows; right: monitoring and remediation loops.

Acceptance Criteria:

  • Shows inheritance across layers
  • RBAC roles and example permissions shown
  • Remediation/monitoring feedback loops illustrated
  • Links to policy definition examples included

Microsoft Learn Adaptation:

Asset 25: Arc Cost Optimization Flows

Section titled “Asset 25: Arc Cost Optimization Flows”

Context: Guides discussions about cost levers and chargeback models for Arc-managed resources, helping presales and architects.

Design Constraints:

  • Canvas 1200x800 px
  • Emphasize flow and before/after comparisons

Content Requirements:

  • Resource consumption flows
  • Cost levers: reserved, spot, right-size, hybrid benefits
  • Analytics & chargeback model

Visual Elements:

  • Dollar-flow diagrams, percentage savings callouts, before/after visuals

Wireframe Guidance: Left: consumption sources; center: cost levers; right: savings/outcome panel

Acceptance Criteria:

  • Shows 4–6 clear cost levers
  • Includes visual savings example
  • Includes chargeback model callout
  • Uses correct palette and icons

Microsoft Learn Adaptation:

Asset 26: Enterprise Deployment Topology

Section titled “Asset 26: Enterprise Deployment Topology”

Context: Shows multi-site Arc-managed topology with central governance and hybrid connectivity options for enterprise deployments.

Design Constraints:

  • Canvas 1400x900 px
  • Include network types (ExpressRoute, VPN), satellite hints optional

Content Requirements:

  • Multiple sites with local resources
  • Central Azure control plane and management agents
  • Connectivity patterns and latency notes

Visual Elements:

  • Geographic site icons, central dashboard, connection line styles for latency

Wireframe Guidance: Map layout: HQ on left, branch/retail on right, cloud control plane top center. Include legend for connection types.

Acceptance Criteria:

  • Shows central management clearly
  • Distinguishes connection types and latency cues
  • Agent communication patterns labeled
  • Resilience and offline scenarios noted

Microsoft Learn Adaptation:

Module 3: Edge RAG Implementation

Section titled “Module 3: Edge RAG Implementation”

Asset 27: Production RAG Architecture (Detailed)

Section titled “Asset 27: Production RAG Architecture (Detailed)”

Context: A production-grade Edge RAG topology with load balancing, HA, vector DB replication, LLM inference services and persistence.

Design Constraints:

  • Canvas 1400x1100 px
  • Show both HA and optional cloud sync (dashed lines)

Content Requirements:

  • Load balancer/ingress
  • Replica services for RAG components
  • Vector DB replication and backup
  • LLM inference cluster (Ollama or equivalent)
  • Ingestion pipeline and storage
  • Monitoring/alerting stack

Visual Elements:

  • Layered sections for ingress, processing, storage, monitoring
  • Colored overlays for optional cloud components

Wireframe Guidance: Top: ingress/load balancer; mid: RAG services and vector DB; bottom: storage and monitoring; right: optional cloud sync dashed overlay.

Acceptance Criteria:

  • HA and replicas are indicated
  • Vector DB replication/backups labeled
  • LLM inference cluster and model instances visible
  • Ingestion pipeline with queue shown
  • Monitoring stack illustrated with metrics/log flow
  • Optional cloud components dashed and labeled

Microsoft Learn Adaptation:

Asset 28: LLM Inference Optimization

Section titled “Asset 28: LLM Inference Optimization”

Context: Visualizes inference optimizations like quantization, batching, caching and hardware acceleration to help engineers trade off latency vs accuracy.

Design Constraints:

  • Canvas 1300x900 px
  • Include performance curves or small charts

Content Requirements:

  • Quantization options, batching strategies, caching (KV cache)
  • Hardware paths: CPU/GPU/NPU options
  • Tradeoffs: latency vs accuracy

Visual Elements:

  • Pipeline diagram with branches for optimization approaches
  • Small performance curves or heatmap

Wireframe Guidance: Left: model baseline; center: optimization branches; right: performance curves and recommended hardware.

Acceptance Criteria:

  • Shows quantization techniques and tradeoffs
  • Shows batching and cache impact
  • Hardware options mapped to performance curves
  • Clear recommendations for edge configurations

Microsoft Learn Adaptation:

Asset 29: Vector Database Architecture Comparison

Section titled “Asset 29: Vector Database Architecture Comparison”

Context: Compares Weaviate, Milvus and PostgreSQL+pgvector across architecture, performance and operational considerations.

Design Constraints:

  • Canvas 1400x1000 px
  • Three-column comparative layout

Content Requirements:

  • Architecture style, indexing, HA, backup, cost and recommended use-cases

Visual Elements:

  • Three columns with micro-architecture sketches, charts and feature matrix

Wireframe Guidance: Three vertical panels: left Weaviate, center Milvus, right pgvector; bottom row: feature matrix and recommendation

Acceptance Criteria:

  • Each DB has architecture sketch
  • Feature matrix compares all critical dimensions
  • Recommendations per use-case present
  • Performance indicators and cost notes included

Microsoft Learn Adaptation:

Asset 30: RAG Deployment Topology Options

Section titled “Asset 30: RAG Deployment Topology Options”

Context: Shows multiple RAG topology templates to map customer scale and availability needs to architecture.

Design Constraints:

  • Canvas 1400x900 px
  • Include four topology variants in grid

Content Requirements:

  • Single-node edge, HA cluster, multi-site federation, hybrid cloud+edge
  • Latency/throughput and cost tradeoffs per variant

Visual Elements:

  • Four topology diagrams in 2x2 grid with short metrics and pros/cons

Wireframe Guidance: Top left: single-node; top right: HA cluster; bottom left: multi-site; bottom right: hybrid cloud+edge

Acceptance Criteria:

  • All four topologies clearly shown
  • Each has latency/cost/scale notes
  • Pros/cons bullets present

Microsoft Learn Adaptation:

Asset 31: RAG Monitoring and Observability

Section titled “Asset 31: RAG Monitoring and Observability”

Context: Provides a map of metrics/logs/traces and alerting for a production RAG deployment, enabling ops to set up observability.

Design Constraints:

  • Canvas 1300x900 px
  • Include Prometheus-style metrics and Azure Monitor integration pointers

Content Requirements:

  • Data collection points, processing pipelines (metrics/logs/traces), alert routing, dashboards

Visual Elements:

  • Pipelines with arrows from components to monitoring stack
  • Dashboard mockups and feedback loop

Wireframe Guidance: Left: RAG components; center: metrics & logs pipelines; right: dashboards & alerts

Acceptance Criteria:

  • Data collection arrows from all major components
  • Monitoring and logging tools labeled (Prometheus, Azure Monitor)
  • Tracing and alert routing visible
  • Dashboard and feedback loop included

Microsoft Learn Adaptation:

Module 4: Pre-Sales & Solution Design

Section titled “Module 4: Pre-Sales & Solution Design”

Asset 32: Customer Discovery Framework

Section titled “Asset 32: Customer Discovery Framework”

Context: Guides presales through discovery phases to map business drivers to solution recommendations.

Design Constraints:

  • Canvas 1200x800 px
  • Funnel or circular process

Content Requirements:

  • Phases 1–5 with key questions
  • Decision points and KPIs
  • Mapping to solution recommendations

Visual Elements:

  • Funnel with callouts, decision nodes, customer profiles

Wireframe Guidance: Circular or funnel flow left-to-right with phase callouts and final recommendation box

Acceptance Criteria:

  • All discovery phases present with key questions
  • Decision tree leads to solution suggestions
  • KPIs and success criteria included

Microsoft Learn Adaptation:

  • Use customer-discovery context in docs/level-200/customer-discovery.md

Asset 33: Solution Sizing Framework

Section titled “Asset 33: Solution Sizing Framework”

Context: Translates customer inputs into compute/storage/network sizing and cost estimates.

Design Constraints:

  • Canvas 1300x800 px
  • Include formula flow and outputs

Content Requirements:

  • Input variables and calculation layers
  • Example outputs and confidence ranges

Visual Elements:

  • Flow diagram with inputs, calculations, and outputs; confidence/range visuals

Wireframe Guidance: Top: inputs; mid: calculation layer with formulas; bottom: outputs and cost/ROI summary

Acceptance Criteria:

  • Inputs, calculation layers, and outputs clearly shown
  • Example numbers or formulas present
  • Confidence ranges included

Microsoft Learn Adaptation:

  • Cross-reference to docs/level-200/solution-sizing.md

Asset 34: TCO and ROI Analysis Model

Section titled “Asset 34: TCO and ROI Analysis Model”

Context: Comparative TCO/ROI modeling for sovereign vs standard cloud to aid decisions.

Design Constraints:

  • Canvas 1400x900 px
  • Include timeline graphs and sensitivity diagrams

Content Requirements:

  • TCO categories, timeline, ROI drivers, break-even analysis

Visual Elements:

  • Stacked cost charts, ROI waterfall and tornado sensitivity

Wireframe Guidance: Top: stacked cost by year; mid: ROI waterfall; bottom: sensitivity chart

Acceptance Criteria:

  • Breakdowns by cost category visible
  • ROI waterfall and break-even point shown
  • Sensitivity analysis present

Microsoft Learn Adaptation:

  • Use cost-estimation guidance in docs/level-200/cost-estimation.md

Module 5: Compliance & Security Patterns

Section titled “Module 5: Compliance & Security Patterns”

Asset 35: GDPR Compliance Mapping

Section titled “Asset 35: GDPR Compliance Mapping”

Context: Maps GDPR articles/principles to technical controls and evidence collection strategies in Azure Local and Arc deployments.

Design Constraints:

  • Canvas 1400x900 px
  • Include three-column layout (requirements → controls → evidence)

Content Requirements:

  • GDPR principles and mapping to Azure controls
  • Evidence examples and audit trace paths

Visual Elements:

  • Three-column map, checkmark color coding, audit trail arrows

Wireframe Guidance: Left: GDPR principles; center: technical controls; right: evidence and reporting flows

Acceptance Criteria:

  • All major GDPR principles mapped
  • Technical controls and service names present
  • Evidence collection pathways shown
  • References to EU Data Boundary where applicable

Microsoft Learn Adaptation:

Asset 36: FedRAMP Compliance Architecture

Section titled “Asset 36: FedRAMP Compliance Architecture”

Context: Shows how Azure Local can be configured to meet FedRAMP control families and the ATO process.

Design Constraints:

  • Canvas 1400x1000 px
  • Highlight control-family mapping and continuous monitoring

Content Requirements:

  • Control family mappings, encryption and access controls, continuous monitoring pipeline

Visual Elements:

  • Layered architecture with control-family overlays and ATO roadmap

Wireframe Guidance: Top: control families; mid: architecture mappings; bottom: monitoring and ATO steps

Acceptance Criteria:

  • Control families annotated and mapped to services
  • Encryption and access controls shown
  • ATO steps and continuous monitoring pipeline illustrated

Microsoft Learn Adaptation:

Asset 37: Encryption and Key Management Architecture

Section titled “Asset 37: Encryption and Key Management Architecture”

Context: Depicts key lifecycle, HSM integration, BYOK/BYOHSM options, and where keys are used across systems.

Design Constraints:

  • Canvas 1400x1000 px
  • Use key lifecycle flow and pyramid hierarchy

Content Requirements:

  • Key hierarchy, lifecycle stages, Key Vault/HSM integration, access controls, audit trails

Visual Elements:

  • Pyramid for hierarchy, flowchart for lifecycle, management plane with Key Vault icons

Wireframe Guidance: Top left: key hierarchy; top right: lifecycle flow; bottom: Key Vault/HSM integration and access controls

Acceptance Criteria:

  • Key lifecycle shown with stages
  • BYOK and HSM options depicted
  • Integration with Key Vault illustrated
  • Access control points and audits included

Microsoft Learn Adaptation:

Asset 38: Zero-Trust Security Architecture

Section titled “Asset 38: Zero-Trust Security Architecture”

Context: Visualizes Zero-Trust applied to identities, devices, networks, apps and data in sovereign cloud deployments.

Design Constraints:

  • Canvas 1300x1000 px
  • Centralized core with rings for pillars

Content Requirements:

  • Core principle (verify, assume breach, secure layers) and mapping to Azure services

Visual Elements:

  • Central core with radial pillars, service icons around ring, detection/response loop

Wireframe Guidance: Core center: verify icon; surrounding rings: identity, endpoints, networks, data, apps; outer: detection & response

Acceptance Criteria:

  • Core Zero-Trust principle shown
  • Pillars mapped to Azure services
  • Detection and response loop shown

Microsoft Learn Adaptation:

Summary & Next Steps

Section titled “Summary & Next Steps”
  • Total assets specified: 18 (Assets 21–38)
  • Each asset includes context, constraints, content, visuals, wireframes, acceptance criteria, and Learn references
  • Next: integrate placeholders into docs/level-200/*.md (Phase 3)

Last Updated: October 21, 2025