AWS · Certification path · AIP-C01
AIP-C01: Generative AI Developer - Professional
35 stages in study order, about 14.5 hours to complete, drawn from AI and machine learning and Security. Each stage names the section of the AIP-C01 exam guide it practises.
The path, in study order
- 1Right ToolFreeAI and machine learning · AI World 1 · Skill 1.3.2, Skill 1.3.4
- 2Pick the ModelAI and machine learning · AI World 1 · Skill 1.2.1, Skill 2.2.1, Skill 2.2.3, Skill 4.1.2, Skill 4.2.1, Skill 4.2.3
- 3Shape the AnswerAI and machine learning · AI World 1 · Skill 1.6.1, Skill 1.6.3, Skill 1.6.5, Skill 4.2.4, Skill 5.2.1
- 4Teach and MeasureAI and machine learning · AI World 1 · Skill 1.2.4, Skill 5.1.1, Skill 5.1.2, Skill 5.1.3, Skill 5.1.5
- 5Guard the GateAI and machine learning · AI World 1 · Skill 1.6.1, Skill 3.1.1, Skill 3.1.2, Skill 3.1.3, Skill 3.1.5, Skill 3.2.2, Skill 3.2.3
- 6Govern ItAI and machine learning · AI World 1 · Skill 2.3.3, Skill 3.2.2, Skill 3.3.1, Skill 3.3.2, Skill 3.4.1, Skill 3.4.3
- 7Feed the BaseAI and machine learning · AI World 2 · Skill 1.4.4, Skill 1.4.5
- 8Cut and EmbedAI and machine learning · AI World 2 · Skill 1.5.1, Skill 1.5.2, Skill 5.2.1
- 9Pick the StoreAI and machine learning · AI World 2 · Skill 1.4.1, Skill 1.4.3, Skill 1.5.3
- 10Find the Right ChunkAI and machine learning · AI World 2 · Skill 1.4.2, Skill 1.5.4, Skill 1.5.5, Skill 1.5.6
- 11Prove the RetrievalAI and machine learning · AI World 2 · Skill 5.1.1, Skill 5.1.5, Skill 5.1.6
- 12Nothing FoundAI and machine learning · AI World 2 · Skill 5.2.1, Skill 5.2.4
- 13Give It ToolsAI and machine learning · AI World 3 · Skill 1.5.6, Skill 2.1.6, Skill 2.1.7
- 14Run It SomewhereAI and machine learning · AI World 3 · Skill 2.1.1, Skill 2.5.5
- 15Remember MeAI and machine learning · AI World 3 · Skill 2.1.1
- 16Know When to StopAI and machine learning · AI World 3 · Skill 2.1.3, Skill 2.3.3
- 17Guard the AgentAI and machine learning · AI World 3 · Skill 3.1.1, Skill 3.1.4, Skill 3.1.5, Skill 3.2.3
- 18Read the TraceAI and machine learning · AI World 3 · Skill 3.4.1, Skill 4.3.4, Skill 5.1.7
- 19Shape the DataAI and machine learning · AI World 4 · Skill 1.3.2
- 20Label ItAI and machine learning · AI World 4 · Skill 1.3.2
- 21Check the BiasAI and machine learning · AI World 4 · Skill 3.3.1, Skill 3.4.2
- 22Read the NumbersAI and machine learning · AI World 4 · Skill 5.1.4
- 23Wire the PipelineAI and machine learning · AI World 4 · Skill 5.1.4, Skill 5.1.9
- 24Pick the EndpointAI and machine learning · AI World 5 · Skill 2.2.1, Skill 2.2.2
- 25Scale ItAI and machine learning · AI World 5 · Skill 4.1.3, Skill 4.2.5
- 26Keep It PrivateAI and machine learning · AI World 5 · Skill 2.3.3, Skill 3.2.1
- 27Lock It DownAI and machine learning · AI World 5 · Skill 2.3.3, Skill 3.3.2
- 28Test in ProductionAI and machine learning · AI World 5 · Skill 5.1.2, Skill 5.1.9
- 29Watch the DriftAI and machine learning · AI World 5 · Skill 3.3.4, Skill 4.3.1, Skill 4.3.2, Skill 4.3.3
- 30Tag Says SoSecurity · Security World 1 · Skill 2.1.3
- 31Inside the BoundarySecurity · Security World 1 · Skill 2.1.3
- 32Who Are YouSecurity · Security World 1 · Skill 2.3.3
- 33On the WireSecurity · Security World 3 · Skill 3.2.1
- 34Secrets and MasksSecurity · Security World 3 · Skill 3.2.2
- 35Log What MattersSecurity · Security World 5 · Skill 3.3.2
Articles on AIP-C01
MLA-C02 · AI and machine learning
Amazon Bedrock or SageMaker AI: the question that decides
Bedrock serves foundation models through an API. SageMaker AI builds, trains and hosts your own. The deciding question is where the answer has to come from.
Ends at Right ToolFree5 min read · video coming
AIF-C01 · AI and machine learning
Which AWS AI service for which request: read the input, then the output
Audio, scans, photos, text or rows with known outcomes. The form of the input and the output asked for pick the AWS AI service, and rule out the near misses.
Ends at Right ToolFree5 min read · video coming
Guide sections the path practises
| Section | As the guide words it | Stages |
|---|---|---|
| Skill 1.2.1 | Assess and choose FMs to ensure optimal alignment with specific business use cases and technical requirements (for example, by using performance benchmarks, capability analysis, limitation evaluation) | 1 |
| Skill 1.2.4 | Implement FM customization deployment and lifecycle management (for example, by using Amazon SageMaker AI to deploy domain-specific fine-tuned models, parameter-efficient adaptation techniques) | 1 |
| Skill 1.3.2 | Create data processing workflows to handle complex data types including text, image, audio, and tabular data with specialized processing requirements for FM consumption (for example, by using Amazon Bedrock multimodal models, SageMaker Processing, AWS Transcribe) | 3 |
| Skill 1.3.4 | Enhance input data quality to improve FM response quality and consistency (for example, by using Amazon Bedrock to reformat text, Amazon Comprehend to extract entities, Lambda functions to normalize data) | 1 |
| Skill 1.4.1 | Create advanced vector database architectures specifically for FM augmentation to enable efficient semantic retrieval beyond traditional search capabilities (for example, by using Amazon Bedrock Knowledge Bases for hierarchical organization, Amazon OpenSearch Service with the Neural plugin for Amazon Bedrock integration for topic-based segmentation, Amazon RDS with Amazon S3 document repositories, Amazon DynamoDB with vector databases for metadata and embeddings) | 1 |
| Skill 1.4.2 | Develop comprehensive metadata frameworks to improve search precision and context awareness for FM interactions (for example, by using S3 object metadata for document timestamps, custom attributes for authorship information, tagging systems for domain classification) | 1 |
| Skill 1.4.3 | Implement high-performance vector database architectures to optimize semantic search performance at scale for FM retrieval (for example, by using OpenSearch sharding strategies, multi-index approaches for specialized domains, hierarchical indexing techniques) | 1 |
| Skill 1.4.4 | Use AWS services to create integration components to connect with resources (for example, document management systems, knowledge bases, internal wikis for comprehensive data integration in GenAI applications) | 1 |
| Skill 1.4.5 | Design and deploy data maintenance systems to ensure that vector stores contain current and accurate information for FM augmentation (for example, by using incremental update mechanisms, real-time change detection systems, automated synchronization workflows, scheduled refresh pipelines) | 1 |
| Skill 1.5.1 | Develop effective document segmentation approaches to optimize retrieval performance for FM context augmentation (for example, by using Amazon Bedrock chunking capabilities, Lambda functions to implement fixed-size chunking, custom processing for hierarchical chunking based on content structure) | 1 |
| Skill 1.5.2 | Select and configure optimal embedding solutions to create efficient vector representations for semantic search (for example, by using Amazon Titan embeddings based on dimensionality and domain fit, by evaluating performance characteristics of Amazon Bedrock embedding models, by using Lambda functions to batch generate embeddings) | 1 |
| Skill 1.5.3 | Deploy and configure vector search solutions to enable semantic search capabilities for FM augmentation (for example, by using OpenSearch Service with vector search capabilities, Amazon Aurora with the pgvector extension, Amazon Bedrock Knowledge Bases with managed vector store functionality) | 1 |
| Skill 1.5.4 | Create advanced search architectures to improve the relevance and accuracy of retrieved information for FM context (for example, by using OpenSearch for semantic search, hybrid search that combines keywords and vectors, Amazon Bedrock reranker models) | 1 |
| Skill 1.5.5 | Develop sophisticated query handling systems to improve the retrieval effectiveness and result quality for FM augmentation (for example, by using Amazon Bedrock for query expansion, Lambda functions for query decomposition, Step Functions for query transformation) | 1 |
| Skill 1.5.6 | Create consistent access mechanisms to enable seamless integration with FMs (for example, by using function calling interfaces for vector search, Model Context Protocol [MCP] clients for vector queries, standardized API patterns for retrieval augmentation) | 2 |
| Skill 1.6.1 | Create effective model instruction frameworks to control FM behavior and outputs (for example, by using Amazon Bedrock Prompt Management to enforce role definitions, Amazon Bedrock Guardrails to enforce responsible AI guidelines, template configurations to format responses) | 2 |
| Skill 1.6.3 | Implement comprehensive prompt management and governance systems to ensure consistency and oversight of FM operations (for example, by using Amazon Bedrock Prompt Management to create parameterized templates and approval workflows) | 1 |
| Skill 1.6.5 | Enhance FM performance to refine prompts iteratively and improve response quality beyond basic prompting techniques (for example, by using structured input components, output format specifications, chain-of-thought instruction patterns, feedback loops) | 1 |
| Skill 2.1.1 | Develop intelligent autonomous systems with appropriate memory and state management capabilities (for example, by using Strands Agents and AWS Agent Squad for multi-agent systems, MCP for agent-tool interactions) | 2 |
| Skill 2.1.3 | Develop safeguarded AI workflows to ensure controlled FM behavior (for example, by using Step Functions to implement stopping conditions, Lambda functions to implement timeout mechanisms, IAM policies to enforce resource boundaries, circuit breakers to mitigate failures) | 3 |
| Skill 2.1.6 | Implement intelligent tool integrations to extend FM capabilities and to ensure reliable tool operations (for example, by using the Strands API to implement custom behaviors, standardized function definitions, Lambda functions to implement error handling and parameter validation) | 1 |
| Skill 2.1.7 | Develop model extension frameworks to enhance FM capabilities (for example, by using Lambda functions to implement stateless MCP servers that provide lightweight tool access, Amazon ECS to implement MCP servers that provide complex tools, MCP client libraries to ensure consistent access patterns) | 1 |
| Skill 2.2.1 | Deploy FMs based on specific application needs and performance requirements (for example, by using Lambda functions for on-demand invocation, Amazon Bedrock provisioned throughput configurations, SageMaker AI endpoints to implement hybrid solutions) | 2 |
| Skill 2.2.2 | Deploy FM solutions by addressing unique challenges of large language models (LLMs) that differ from traditional ML deployments (for example, by implementing container-based deployment patterns that are optimized for memory requirements, GPU utilization, and token processing capacity, by following specialized model loading strategies) | 1 |
| Skill 2.2.3 | Develop optimized FM deployment approaches to balance performance and resource requirements for GenAI workloads (for example, by selecting appropriate models, by using smaller pre-trained models for specific tasks, by using API-based model cascading to perform routine queries) | 1 |
| Skill 2.3.3 | Create secure access frameworks to ensure appropriate security controls (for example, by using identity federation between FM services and enterprise systems, role-based access control for model and data access, least privilege API access to FMs) | 5 |
| Skill 2.5.5 | Develop advanced GenAI applications to implement sophisticated AI capabilities (for example, by using Strands Agents and AWS Agent Squad for AWS native orchestration, Step Functions to orchestrate agent design patterns, Amazon Bedrock to manage prompt chaining patterns) | 1 |
| Skill 3.1.1 | Develop comprehensive content safety systems to protect against harmful user inputs to FMs (for example, by using Amazon Bedrock guardrails to filter content, Step Functions and Lambda functions to implement custom moderation workflows, real-time validation mechanisms) | 2 |
| Skill 3.1.2 | Create content safety frameworks to prevent harmful outputs (for example, by using Amazon Bedrock guardrails to filter responses, specialized FM evaluations for content moderation and toxicity detection) | 1 |
| Skill 3.1.3 | Develop accuracy verification systems to reduce hallucinations in FM responses (for example, by using Amazon Bedrock Knowledge Base to ground responses and perform fact-checking, confidence scoring and semantic similarity search for verification, JSON Schema to enforce structured outputs) | 1 |
| Skill 3.1.4 | Create defense-in-depth safety systems to provide comprehensive protection against FM misuse (for example, by using Amazon Comprehend to develop pre-processing filters, Amazon Bedrock to implement model-based guardrails, Lambda functions to perform post-processing validation, API Gateway to implement API response filtering) | 1 |
| Skill 3.1.5 | Implement advanced threat detection to protect against adversarial inputs and security vulnerabilities (for example, by using prompt injection and jailbreak detection mechanisms, input sanitization and content filters, safety classifiers, automated adversarial testing workflows) | 2 |
| Skill 3.2.1 | Develop protected AI environments to ensure comprehensive security for FM deployments (for example, by using VPC endpoints to isolate networks, IAM policies to enforce secure data access patterns, AWS Lake Formation to provide granular data access, CloudWatch to monitor data access) | 2 |
| Skill 3.2.2 | Develop privacy-preserving systems to protect sensitive information during FM interactions (for example, by using Amazon Comprehend and Amazon Macie to detect personally identifiable information [PII], Amazon Bedrock native data privacy features, Amazon Bedrock guardrails to filter outputs) | 3 |
| Skill 3.2.3 | Create privacy-focused AI systems to protect user privacy while maintaining FM utility and effectiveness (for example, by using data masking techniques, Amazon Comprehend PII detection, anonymization strategies for sensitive information, Amazon Bedrock guardrails) | 2 |
| Skill 3.3.1 | Develop compliance frameworks to ensure regulatory compliance for FM deployments (for example, by using SageMaker AI to develop programmatic model cards, AWS Glue to automatically track data lineage, metadata tagging for systematic data source attribution, CloudWatch Logs to collect comprehensive decision logs) | 2 |
| Skill 3.3.2 | Implement data source tracking to maintain traceability in GenAI applications (for example, by using AWS Glue Data Catalog to register data sources, metadata tagging for source attribution in FM-generated content, CloudTrail for audit logging) | 3 |
| Skill 3.3.4 | Implement continuous monitoring and advanced governance controls to support safety audits and regulatory readiness (for example, by using automated detection for misuse, drift, and policy violations, bias drift monitoring, automated alerting and remediation workflows, token-level redaction, response logging, AI output policy filters) | 1 |
| Skill 3.4.1 | Develop transparent AI systems in FM outputs (for example, by using reasoning displays to provide user-facing explanations, CloudWatch to collect confidence metrics and quantify uncertainty, evidence presentation for source attribution) | 2 |
| Skill 3.4.2 | Apply fairness evaluations to ensure unbiased FM outputs (for example, by using pre-defined fairness metrics in CloudWatch, Amazon Bedrock Prompt Management and Amazon Bedrock Prompt Flows to perform systematic A/B testing, Amazon Bedrock with LLM-as-a-judge solutions to perform automated model evaluations) | 1 |
| Skill 3.4.3 | Develop policy-compliant AI systems to ensure adherence to responsible AI practices (for example, by using Amazon Bedrock guardrails based on policy requirements, model cards to document FM limitations, Lambda functions to perform automated compliance checks) | 1 |
| Skill 4.1.2 | Create cost-effective model selection frameworks (for example, by using cost-capability tradeoff evaluation, tiered FM usage based on query complexity, inference cost balancing against response quality, price-to-performance ratio measurement, efficient inference patterns) | 1 |
| Skill 4.1.3 | Develop high-performance FM systems to maximize resource utilization and throughput for GenAI workloads (for example, by using batching strategies, capacity planning, utilization monitoring, auto-scaling configurations, provisioned throughput optimization) | 1 |
| Skill 4.2.1 | Create responsive AI systems to address latency-cost tradeoffs and improve the user experience with FMs (for example, by using pre-computation to perform predictable queries, latency-optimized Amazon Bedrock models for time-sensitive applications, parallel requests for complex workflows, response streaming, performance benchmarking) | 1 |
| Skill 4.2.3 | Implement FM throughput optimization to address the specific throughput challenges of GenAI workloads (for example, by using token processing optimization, batch inference strategies, concurrent model invocation management) | 1 |
| Skill 4.2.4 | Enhance FM performance to achieve optimal results for specific GenAI use cases (for example, by using model-specific parameter configurations, A/B testing to evaluate improvements, appropriate temperature and top-k/top-p selection based on requirements) | 1 |
| Skill 4.2.5 | Create efficient resource allocation systems specifically for FM workloads (for example, by using capacity planning for token processing requirements, utilization monitoring for prompt and completion patterns, auto-scaling configurations that are optimized for GenAI traffic patterns) | 1 |
| Skill 4.3.1 | Create holistic observability systems to provide complete visibility into FM application performance (for example, by using operational metrics, performance tracing, FM interaction tracing, business impact metrics with custom dashboards) | 1 |
| Skill 4.3.2 | Implement comprehensive GenAI monitoring systems to proactively identify issues and evaluate key performance indicators specific to FM implementations (for example, by using CloudWatch to track token usage; prompt effectiveness; hallucination rates; and response quality, anomaly detection for token burst patterns and response drift, Amazon Bedrock Model Invocation Logs to perform detailed request and response analysis, performance benchmarks, cost anomaly detection) | 1 |
| Skill 4.3.3 | Develop integrated observability solutions to provide actionable insights for FM applications (for example, by using operational metric dashboards, business impact visualizations, compliance monitoring, forensic traceability and audit logging, user interaction tracking, model behavior pattern tracking) | 1 |
| Skill 4.3.4 | Create tool performance frameworks to ensure optimal tool operation and utilization for FMs (for example, by using call pattern tracking, performance metric collection, tool calling observability and multi-agent coordination tracking, usage baselines for anomaly detection) | 1 |
| Skill 5.1.1 | Develop comprehensive assessment frameworks to evaluate the quality and effectiveness of FM outputs beyond traditional ML evaluation approaches (for example, by using metrics for relevance, factual accuracy, consistency, and fluency) | 2 |
| Skill 5.1.2 | Create systematic model evaluation systems to identify optimal configurations (for example, by using Amazon Bedrock Model Evaluations, A/B testing and canary testing of FMs, multi-model evaluation, cost-performance analysis) | 2 |
| Skill 5.1.3 | Develop user-centered evaluation mechanisms to continuously improve FM performance based on user experience (for example, by using feedback interfaces, rating systems for model outputs, annotation workflows to assess response quality) | 1 |
| Skill 5.1.4 | Create systematic quality assurance processes to maintain consistent performance standards for FMs (for example, by using continuous evaluation workflows, regression testing for model outputs, automated quality gates for deployments) | 2 |
| Skill 5.1.5 | Develop comprehensive assessment systems to ensure thorough evaluation from multiple perspectives for FM outputs (for example, by using RAG evaluation, automated quality assessment with LLM-as-a-Judge techniques, human feedback collection interfaces) | 2 |
| Skill 5.1.6 | Implement retrieval quality testing to evaluate and optimize information retrieval components for FM augmentation (for example, by using relevance scoring, context matching verification, retrieval latency measurements) | 1 |
| Skill 5.1.7 | Develop agent performance frameworks to ensure that agents perform tasks correctly and efficiently (for example, by using task completion rate measurements, tool usage effectiveness evaluations, Amazon Bedrock Agent evaluations, reasoning quality assessment in multi-step workflows) | 1 |
| Skill 5.1.9 | Create deployment validation systems to maintain reliability during FM updates (for example, by using synthetic user workflows, AI-specific output validation for hallucination rates and semantic drift, automated quality checks to ensure response consistency) | 2 |
| Skill 5.2.1 | Resolve content handling issues to ensure that necessary information is processed completely in FM interactions (for example, by using context window overflow diagnostics, dynamic chunking strategies, prompt design optimization, truncation-related error analysis) | 3 |
| Skill 5.2.4 | Troubleshoot retrieval system issues to identify and resolve problems that affect information retrieval effectiveness for FM augmentation (for example, by using model response relevance analysis, embedding quality diagnostics, drift monitoring, vectorization issue resolution, chunking and preprocessing remediation, vector search performance optimization) | 1 |
A domain and a path are separate products. What you own is the union of what you bought, so a stage you already own is never sold to you twice: the game offers the cheapest way to unlock the stage in front of you. Path $30, exam $300.