AIP-C01: AWS Generative AI Developer Last-Minute Refresher
Final Review
This page is designed for the final "cram" session before stepping into the AIP-C01 exam.
Domain 1: FM Integration, Data Management, and Compliance (~31%)
FM Selection Quick Guide
| FM | Vendor | Best For |
|---|---|---|
| Claude | Anthropic | Reasoning, large context (up to 200k tokens) |
| Llama | Meta | Open-source, fine-tuning |
| Mistral | Mistral AI | Efficient, high performance for size |
| Titan | AWS | Embeddings, summarization, AWS-native |
RAG Pipeline
Important Points:
- S3 → chunking → embedding → OpenSearch Serverless (vector store)
- Knowledge Bases manage this pipeline fully managed
- pgvector on Aurora PostgreSQL = alternative vector store
- Chunking options: fixed-size, semantic, hierarchical
Domain 2: Implementation and Integration (~26%)
API Quick Reference
Which API?
├─ Full synchronous response → InvokeModel
├─ Streaming / low latency → InvokeModelWithResponseStream
└─ Multi-step agentic workflow → InvokeAgentComparison Table
| Feature | Knowledge Base (RAG) | Bedrock Agents |
|---|---|---|
| Use Case | Document Q&A, retrieval | Multi-step workflows, tool use |
| FM + Retrieval | Yes (automatic) | Yes (optional) |
| External API calls | No | Yes (via Action Groups + Lambda) |
| Best For | Static knowledge lookup | Dynamic reasoning + actions |
Domain 3: AI Safety, Security, and Governance (~20%)
Guardrails Quick Facts
- Applies to both inputs and outputs
- Features: content filters, PII redaction, denied topics, word filters
- Must be explicitly applied via
guardrailIdentifierin API calls - Does NOT automatically apply to all Bedrock calls
Security Decision Tree
Data must stay private / no public internet?
└─ Yes → VPC Endpoint (PrivateLink) for Bedrock
Need to restrict access to Bedrock models?
└─ Yes → IAM resource-based policies
Need to audit all FM API calls?
└─ Yes → AWS CloudTrail (not CloudWatch)
Need operational metrics (latency, errors)?
└─ Yes → Amazon CloudWatchDomain 4: Operational Efficiency and Optimization (~12%)
Provisioned Throughput vs On-Demand
| Provisioned Throughput | On-Demand | |
|---|---|---|
| Traffic | Predictable, consistent | Sporadic, unpredictable |
| Pricing | Fixed (per MU/hour) | Pay per token |
| Commitment | 1 or 6 months | None |
| Best For | Production 24/7 | Dev/test, bursts |
Domain 5: Testing, Validation, and Troubleshooting (~11%)
Model Evaluation Metrics
| Metric | What It Measures |
|---|---|
| Groundedness | Is the response grounded in the retrieved context? |
| Relevance | Is the response relevant to the user's question? |
| Accuracy | Is the response factually correct? |
| Fluency | Is the response well-written and natural? |
Key Service Decision Rules
Question asks about...
- "Predictable throughput" → Provisioned Throughput
- "Content moderation / filter" → Guardrails for Amazon Bedrock
- "PII detection/redaction" → Guardrails (PII filter)
- "Vector storage for RAG" → OpenSearch Serverless (or Aurora pgvector)
- "Multi-step reasoning / tool use" → Bedrock Agents
- "Audit logging / compliance trail" → AWS CloudTrail
- "Operational monitoring / dashboards" → Amazon CloudWatch
- "Private FM access, no internet" → VPC Endpoint (PrivateLink)
- "Low latency / streaming response" → InvokeModelWithResponseStream
Key Acronyms
| Acronym | Full Form | Quick Definition |
|---|---|---|
| FM | Foundation Model | Pre-trained large AI model (Claude, Llama, etc.) |
| RAG | Retrieval-Augmented Generation | FM + vector store retrieval |
| PTU | Provisioned Throughput Unit | Reserved Bedrock model capacity |
| PII | Personally Identifiable Information | Data that identifies an individual |
| MU | Model Unit | Unit of Bedrock Provisioned Throughput |
Common Exam Traps
Watch Out!
- InvokeModel vs Streaming: InvokeModel = full synchronous;
InvokeModelWithResponseStream= chunk-by-chunk - Guardrails scope: Guardrails filter BOTH inputs AND outputs — not just one
- CloudTrail vs CloudWatch: CloudTrail = audit/compliance; CloudWatch = operational metrics
- OpenSearch Serverless: The go-to for Knowledge Bases — not standard OpenSearch managed cluster
- PTU timing: On-demand is better for sporadic traffic — don't default to PTU
Last 5 Minutes Before Exam
Must Remember
- Guardrails — apply to both inputs AND outputs; must be explicitly applied per call
- PTU — predictable traffic only; 1- or 6-month commitment
- RAG vector store — OpenSearch Serverless (primary), pgvector on Aurora (alternative)
- InvokeModelWithResponseStream — for low-latency streaming UX
- CloudTrail — required for compliance/audit; CloudWatch for ops monitoring
Quick Mental Check
- Can you explain the difference between RAG and fine-tuning? ✓
- Do you know when to use InvokeModel vs InvokeModelWithResponseStream? ✓
- Can you describe what Guardrails filters cover? ✓
- Do you understand when PTU is preferred over on-demand? ✓
- Can you pick the right vector store for a Knowledge Base? ✓
You've Got This!
Take a deep breath. Review the decision trees. Trust your preparation. Good luck!