LLD ↔ Domain 2 Skills Cross-Reference

Use this file in two directions:

Studying for AWS AIP-C01 → Find the topic deep-dive and LLD questions that give you a real implementation example for each skill.
Preparing for LLD interviews → See which Domain 2 skills each topic reinforces so you can frame answers in certification language.

Topic Deep-Dive → Domain 2 Skills

Topic	LLD Questions Covered	Domain 2 Skills Reinforced
01-orchestrator-request-flow	Q1, Q11, Q24, Q38, Q46	2.1.1 · 2.1.2 · 2.1.3 · 2.4.1 · 2.4.3
02-intent-classification-entity-resolution	Q6, Q7, Q21, Q31, Q32	2.1.2 · 2.1.4
03-conversation-memory-session-storage	Q5, Q8, Q9, Q16, Q22, Q28, Q35, Q36, Q39	2.1.1 · 2.3.1
04-rag-indexing-retrieval-re-ranking	Q12, Q13, Q14, Q15, Q23, Q26, Q30, Q34	2.1.6 · 2.1.7 · 2.5.5
05-guardrails-validation-safety	Q17, Q19, Q20, Q27, Q33, Q42, Q48	2.1.3 · 2.3.3
06-api-contracts-streaming-and-escalation	Q2, Q3, Q4, Q10, Q18, Q25, Q29, Q44, Q45	2.1.5 · 2.4.2 · 2.5.1
07-analytics-observability-and-feedback-loops	Q24, Q37, Q40, Q47, Q49	2.3.2 · 2.5.6
08-scale-failures-and-architecture-evolution	Q31, Q41, Q43, Q45, Q48, Q50	2.2.1 · 2.3.4 · 2.4.3 · 2.4.4

Domain 2 Skill → Topic Deep-Dive

Task 2.1 — Agentic AI Solutions

Skill	Full Name	Topic Deep-Dives
2.1.1	Develop Intelligent Autonomous Systems with Memory and State Management	01-orchestrator · 03-memory
2.1.2	Create Advanced Problem-Solving Systems (ReAct, Chain-of-Thought)	01-orchestrator · 02-intent
2.1.3	Develop Safeguarded AI Workflows	01-orchestrator · 05-guardrails
2.1.4	Create Sophisticated Model Coordination Systems	02-intent
2.1.5	Develop Collaborative AI Systems (Human-in-the-Loop)	06-api-streaming — escalation section only
2.1.6	Implement Intelligent Tool Integrations	04-rag
2.1.7	Develop Model Extension Frameworks (MCP Servers)	04-rag

Task 2.2 — Model Deployment Strategies

Skill	Full Name	Topic Deep-Dives
2.2.1	Deploy FMs Based on Application Needs and Performance Requirements	08-scale
2.2.2	Deploy FM Solutions Addressing Unique LLM Challenges	Gap — no dedicated deep-dive
2.2.3	Develop Optimized FM Deployment Approaches	Gap — no dedicated deep-dive

Task 2.3 — Enterprise Integration

Skill	Full Name	Topic Deep-Dives
2.3.1	Create Enterprise Connectivity Solutions	03-memory — storage layer only
2.3.2	Develop Integrated AI Capabilities for Existing Applications	07-analytics — event pipeline only
2.3.3	Create Secure Access Frameworks	05-guardrails
2.3.4	Develop Cross-Environment AI Solutions	08-scale
2.3.5	Implement CI/CD Pipelines and GenAI Gateway Architectures	Gap — no dedicated deep-dive

Task 2.4 — FM API Integrations

Skill	Full Name	Topic Deep-Dives
2.4.1	Create Flexible Model Interaction Systems	01-orchestrator
2.4.2	Develop Real-Time AI Interaction Systems (Streaming)	06-api-streaming
2.4.3	Create Resilient FM Systems	01-orchestrator · 08-scale
2.4.4	Develop Intelligent Model Routing Systems	08-scale

Task 2.5 — Application Integration Patterns

Skill	Full Name	Topic Deep-Dives
2.5.1	Create FM API Interfaces for GenAI Workloads	06-api-streaming
2.5.2	Develop Accessible AI Interfaces	Gap — no dedicated deep-dive
2.5.3	Create Business System Enhancements	Gap — no dedicated deep-dive
2.5.4	Enhance Developer Productivity	Gap — no dedicated deep-dive
2.5.5	Develop Advanced GenAI Applications	04-rag
2.5.6	Improve Troubleshooting Efficiency for FM Applications	07-analytics

Coverage Gaps — Skills With No LLD Deep-Dive

These 6 skills have no (or only surface-level) LLD question coverage. Adding a topic deep-dive or a set of targeted questions for each would close the gap.

Skill	Gap Description	Suggested New Topic
2.2.2	LLM-specific deployment challenges (cold starts, context window limits, streaming handshake, token budget enforcement) not tested in any current question	`09-fm-deployment-and-llm-challenges.md`
2.2.3	Quantisation, distillation, and inference optimisation not in LLD questions — only touched in Fine-Tuning docs	Add to `09-fm-deployment-and-llm-challenges.md`
2.3.5	CI/CD pipeline for model deployment, knowledge base refresh pipeline, and GenAI gateway pattern not in LLD questions	`10-cicd-and-genai-gateway.md`
2.5.2	Chat widget embedding, streaming UI rendering, and reconnect UX not in any LLD question	Add questions to `06-api-contracts-streaming-and-escalation.md`
2.5.3	Order write-back, CRM case creation, and analytics pipeline fanout not directly tested	Add questions to `07-analytics-observability-and-feedback-loops.md`
2.5.4	Internal SDK design, MCP schema versioning, integration test harness for tool contracts not covered	`10-cicd-and-genai-gateway.md` or new file

How to Use This During Interview Prep

Scenario A — exam-first: Pick a skill you are weak on. Find its row above. Open the linked topic deep-dive and answer the Primary Prompt cold before reading the Deep Dive section. Then read the skill runbooks in Implementation-Integration-Domain2/ for the implementation detail.

Scenario B — interview-first: You have an LLD interview tomorrow on streaming and API design. Open topic 06. After answering each follow-up, check which skills (2.4.2, 2.5.1, 2.1.5) you just practiced — this helps you frame answers in terms the interviewer expects.

Scenario C — gap-filling: Pick one of the six gap skills above. Find the suggested new topic file. Write 3–5 questions in the style of lld-interview-questions.md (question + expected answer). This is also good practice for the exam.