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Welcome, Solutions Architects. I’m Michael Forrester. This lesson introduces the interactive drag-and-drop design challenge for Solutions Architects on KodeKloud. It’s our first time using this interactive format, so the goal here is a concise orientation to the interface, the reasoning approach, and sample scenarios you’ll encounter. Overview
  • Each challenge presents an architecture diagram with one or more missing services.
  • The interface shows:
    • Instructions (upper-left)
    • The architecture diagram
    • A palette of candidate services you can drag into the diagram
    • One or more gray placeholder boxes representing the missing components
  • After placing services into placeholders, click Submit to validate your choices. You can zoom the diagram for clarity.
Tip: Tackle one placement at a time. If a placement is incorrect, use the interface’s restart option to try again — learning by elimination is an effective strategy.
How to reason about each challenge
  • Focus on functional intent, not brand names. Ask: is this component serving transfer, hosting, orchestration, search, conversational intent, or data aggregation?
  • Use diagram hints: icons, labels (e.g., “agent”, “ingestion”, “processing”), telemetry arrows, and placement relative to other services.
  • Narrow choices by capability (e.g., data transfer vs. storage hosting; managed container orchestration vs. VM hosting).
Example 1 — Laboratory Instrument Logs: storage-to-storage synchronization Scenario summary
  • A pipeline captures and analyzes laboratory instrument logs.
  • A hidden service synchronizes files from on-premises storage to Amazon S3 to enable downstream processing and analytics.
  • Requirements: high-speed, automated transfers with metadata coordination and incremental sync.
Candidate services — quick comparison Conclusion: AWS DataSync is the best fit for reliable storage-to-storage synchronization into S3. Drag DataSync into the gray placeholder and submit to validate. Example 2 — Container orchestration, monitoring, and database scalability Scenario summary
  • Development teams need container orchestration, telemetry ingestion for monitoring, and a scalable relational database for many applications.
  • The diagram shows agents sending telemetry to a SaaS monitoring service (Instana), stream processing, a UI backend, and a data store for application data.
Diagram hints and reasoning
  • Container orchestration: look for container icons and labels. Choose a managed container service (EKS, ECS, or managed OpenShift/ROSA) rather than EC2 or Lambda.
  • Database scalability: for high-scale, cloud-native relational workloads, prefer Amazon Aurora (a managed RDS engine built for scale and high performance).
  • Monitoring ingestion: identify the service that accepts agent telemetry and forwards or integrates with the monitoring pipeline (the component labelled or depicted as telemetry ingestion).
Suggested answers (map for quick reference) If a placement is incorrect, revisit the diagram focusing on container-related icons, telemetry flow, and database scale requirements. Example 3 — AI/ML contact center with agent assist and enterprise search Scenario summary
  • A contact center architecture uses AI/ML for agent assist, enterprise search, and data aggregation across sources.
  • The flow typically includes Amazon Connect (call ingestion), Transcribe (speech-to-text), Kinesis + Lambda (stream processing), Comprehend (text analytics), Bedrock (GenAI), and agent-facing components.
The three missing services and how to decide
  • Searchable enterprise information: If the diagram needs a service that indexes and searches across documents and knowledge bases, choose Amazon Kendra.
  • Conversational interface: For intent interpretation and dialog management for chatbots/voice interactions, choose Amazon Lex.
  • Data synchronization/aggregation: For aggregating or exposing multiple data sources through a single API and supporting offline sync patterns for agent apps, choose AWS AppSync (managed GraphQL).
Quick mapping for this scenario How to validate: Drag Kendra for enterprise search, Lex for conversational interfaces, and AppSync for data aggregation into their respective placeholders. If any placement fails validation, re-check the diagram to confirm which layers correspond to search, conversation, and data aggregation.
A technical architecture diagram showing IBM Instana SaaS connected to a customer AWS environment and a corporate data center. It illustrates Instana agents, AWS components (EKS/ECS/ROSA, Lambda, DynamoDB, S3, MSK, SQS), a stream processor/UI backend, and data/alerting flows between them.
A technical diagram of an AI/ML‑powered contact center architecture showing call ingestion, processing, enrichment, and agent-facing flows with numbered components. It includes AWS service icons (e.g., Amazon Connect, Transcribe, Kinesis, Lambda, Comprehend, Bedrock) and a base-architecture icon bar at the bottom.
Final notes
  • Challenges vary: some require a single service, others require multiple placements. Use elimination and the diagram’s functional hints (icons, flow arrows, labels).
  • When choosing between related services, focus on functionality: transfer vs. hosting, orchestration vs. VMs, search vs. index, conversational vs. analytics, GraphQL aggregation vs. point-to-point APIs.
  • If you’re unsure, discuss edge cases on Slack or Discord with peers and instructors.
Links and references If you have questions or want walkthroughs of specific scenarios, reach out on Slack or Discord — and good luck with the design challenge.

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