AIP-C01 Preparation Details
Preparing for the AIP-C01 AWS Certified Generative AI Developer Professional certification exam? Start here with a complete, objective-wise AIP-C01 study guide designed to help you pass faster.
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AWS Generative AI Developer Prep
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Content Domain 1: Foundation Model Integration, Data Management, and Compliance (31% of scored content)
Task 1.1: Analyze requirements and design GenAI solutions.
Skill 1.1.1: Create comprehensive architectural designs that align with specific business needs and technical constraints (for example, by using appropriate FMs, integration patterns, deployment strategies).
Generative AI Lens – AWS Well-Architected Framework
Data architecture – Generative AI Lens
Agentic AI – Generative AI Lens
Architecture overview – Generative AI Application Builder on AWS
Skill 1.1.2: Develop technical proof-of-concept implementations to validate feasibility, performance characteristics, and business value before proceeding to full-scale deployment (for example, by using Amazon Bedrock).
Optimizing generative AI prompts – AWS Prescriptive Guidance
Layer 2: Approved set of foundation models and tools – AWS Prescriptive Guidance
Skill 1.1.3: Create standardized technical components to ensure consistent implementation across multiple deployment scenarios (for example, by using the AWS Well-Architected Framework, AWS WA Tool Generative AI Lens).
Generative AI Lens – AWS Well-Architected Framework
Multi-tenant generative AI platform scenario – Generative AI Lens
SMB/DB knowledge worker co-pilot – Generative AI Lens
Task 1.2: Select and configure FMs.
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).
Choose the best performing model using Amazon Bedrock evaluations
Use metrics to understand model performance – Amazon Bedrock
Evaluate model performance using another LLM as a judge – Amazon Bedrock
Layer 2: Approved set of foundation models and tools – AWS Prescriptive Guidance
Skill 1.2.2: Create flexible architecture patterns to enable dynamic model selection and provider switching without requiring code modifications (for example, by using AWS Lambda, Amazon API Gateway, AWS AppConfig).
Generative AI Lens – AWS Well-Architected Framework
Skill 1.2.3: Design resilient AI systems to ensure continuous operation during service disruptions (for example, by using AWS Step Functions circuit breaker patterns, Amazon Bedrock Cross-Region Inference for models that have limited regional availability, cross-Region model deployment, graceful degradation strategies).
Increase throughput with cross-Region inference – Amazon Bedrock
Geographic cross-Region inference – Amazon Bedrock
Cross-region inference – Amazon Bedrock AgentCore
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 such as low-rank adaptation [LoRA] and adapters for model deployment, SageMaker Model Registry for versioning and to deploy customized models, automated deployment pipelines to update models, rollback strategies for failed deployments, lifecycle management to retire and replace models).
Customize your model to improve its performance – Amazon Bedrock
Multi-tenant generative AI platform scenario – Generative AI Lens
Amazon SageMaker Model Registry
Task 1.3: Implement data validation and processing pipelines for FM consumption.
Skill 1.3.1: Create comprehensive data validation workflows to ensure data meets quality standards for FM consumption (for example, by using AWS Glue Data Quality, SageMaker Data Wrangler, custom Lambda functions, Amazon CloudWatch metrics).
AWS Glue Data Quality – AWS Glue
Getting started with AWS Glue Data Quality for the Data Catalog
Get Insights On Data and Data Quality – Amazon SageMaker AI (Data Wrangler)
Create and Use a Data Wrangler Flow – Amazon SageMaker AI
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, advanced multimodal pipeline architectures).
Data preparation using AWS Glue interactive sessions – Amazon SageMaker AI
Data quality in Amazon SageMaker Unified Studio
Skill 1.3.3: Format input data for FM inference according to model-specific requirements (for example, by using JSON formatting for Amazon Bedrock API requests, structured data preparation for SageMaker AI endpoints, conversation formatting for dialog-based applications).
Design a prompt – Amazon Bedrock
Prompt templates and examples for Amazon Bedrock text models
Advanced prompt templates – Amazon Bedrock
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).
Prompt engineering concepts – Amazon Bedrock
AWS Glue Data Quality – AWS Glue
Task 1.4: Design and implement vector store solutions.
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, Amazon RDS, Amazon DynamoDB with vector databases).
Retrieve data and generate AI responses with Amazon Bedrock Knowledge Bases
How Amazon Bedrock knowledge bases work
Vector database options – AWS Prescriptive Guidance
Knowledge bases for Amazon Bedrock – AWS Prescriptive Guidance
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).
Prerequisites for using a vector store you created for a knowledge base – Amazon Bedrock
Create a knowledge base by connecting to a data source in Amazon Bedrock Knowledge Bases
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).
Vector database options – AWS Prescriptive Guidance
Prerequisites for using a vector store you created for a knowledge base – Amazon Bedrock
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).
Retrieve data and generate AI responses with Amazon Bedrock Knowledge Bases
SMB/DB knowledge worker co-pilot – Generative AI Lens
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).
How Amazon Bedrock knowledge bases work
Create a knowledge base by connecting to a data source in Amazon Bedrock Knowledge Bases
Task 1.5: Design retrieval mechanisms for FM augmentation.
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).
How content chunking works for knowledge bases – Amazon Bedrock
How Amazon Bedrock knowledge bases work
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).
How Amazon Bedrock knowledge bases work
Vector database options – AWS Prescriptive Guidance
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).
Prerequisites for using a vector store you created for a knowledge base – Amazon Bedrock
Create a knowledge base by connecting to a data source in Amazon Bedrock Knowledge Bases
Vector database options – AWS Prescriptive Guidance
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).
How Amazon Bedrock knowledge bases work
Knowledge bases for Amazon Bedrock – AWS Prescriptive Guidance
Layer 2: Approved set of foundation models and tools – AWS Prescriptive Guidance
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).
Retrieve data and generate AI responses with Amazon Bedrock Knowledge Bases
Optimizing generative AI prompts – AWS Prescriptive Guidance
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).
Architecture overview – Generative AI Application Builder on AWS
Retrieve data and generate AI responses with Amazon Bedrock Knowledge Bases
Task 1.6: Implement prompt engineering strategies and governance for FM interactions.
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).
What is prompt engineering? – Amazon Bedrock
Prompt engineering concepts – Amazon Bedrock
Skill 1.6.2: Build interactive AI systems to maintain context and improve user interactions with FMs (for example, by using Step Functions for clarification workflows, Amazon Comprehend for intent recognition, DynamoDB for conversation history storage).
Advanced prompt templates – Amazon Bedrock
Architecture overview – Generative AI Application Builder on AWS
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, Amazon S3 to store template repositories, AWS CloudTrail to track usage, Amazon CloudWatch Logs to log access).
Test a prompt using Prompt management – Amazon Bedrock
Design a prompt – Amazon Bedrock
Skill 1.6.4: Develop quality assurance systems to ensure prompt effectiveness and reliability for FMs (for example, by using Lambda functions to verify expected output, Step Functions to test edge cases, CloudWatch to test prompt regression).
Choose the best performing model using Amazon Bedrock evaluations
Create a model evaluation job using built-in metrics – Amazon Bedrock
Optimizing generative AI prompts – AWS Prescriptive Guidance
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).
Prompt engineering concepts – Amazon Bedrock
Prompt templates and examples for Amazon Bedrock text models
Advanced prompt templates – Amazon Bedrock
Optimizing generative AI prompts – AWS Prescriptive Guidance
Skill 1.6.6: Design complex prompt systems to handle sophisticated tasks with FMs (for example, by using Amazon Bedrock Prompt Flows for sequential prompt chains, conditional branching based on model responses, reusable prompt components, integrated pre-processing and post-processing steps).
Test a prompt using Prompt management – Amazon Bedrock
Architecture overview – Generative AI Application Builder on AWS
Content Domain 2: Implementation and Integration (26% of scored content)
Task 2.1: Implement agentic AI solutions and tool integrations
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)
Overview – Amazon Bedrock AgentCore
Add memory to your Amazon Bedrock AgentCore agent
Strands Agents SDK – Amazon Bedrock AgentCore
Guidance for Multi-Agent Orchestration on AWS
Amazon Bedrock AgentCore – AWS Prescriptive Guidance
Skill 2.1.2: Create advanced problem-solving systems to give FMs the ability to break down and solve complex problems by following structured reasoning steps (for example, by using Step Functions to implement ReAct patterns and chain-of-thought reasoning approaches)
AWS Step Functions Developer Guide
Advanced prompt templates – Amazon Bedrock
Prompt templates and examples for Amazon Bedrock text models
Agents for Amazon Bedrock – Orchestration and reasoning
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)
Safeguard your application with Amazon Bedrock Guardrails
AWS Step Functions error handling
GENOPS02-BP03 Implement solutions to mitigate the risk of system overload – Generative AI Lens
Skill 2.1.4: Create sophisticated model coordination systems to optimize performance across multiple capabilities (for example, by using specialized FMs to perform complex tasks, custom aggregation logic for model ensembles, model selection frameworks)
Understanding intelligent prompt routing in Amazon Bedrock
Choose the best performing model using Amazon Bedrock evaluations
Skill 2.1.5: Develop collaborative AI systems to enhance FM capabilities with human expertise (for example, by using Step Functions to orchestrate review and approval processes, API Gateway to implement feedback collection mechanisms, human augmentation patterns)
AWS Step Functions Developer Guide
Human in the loop for Amazon Bedrock Agents
Build a prompt flow in Amazon Bedrock
Amazon Augmented AI (Amazon A2I) Developer Guide
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)
Use action groups to define actions for Amazon Bedrock Agents
Amazon Bedrock AgentCore Gateway: Securely connect tools and other resources
Define functions to call with the Converse API – Amazon Bedrock
Host agent or tools with Amazon Bedrock AgentCore Runtime
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)
Amazon Bedrock AgentCore – developer guide
Amazon Bedrock AgentCore Gateway: Securely connect tools and other resources
Amazon Elastic Container Service Developer Guide
Task 2.2: Implement model deployment strategies
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)
Increase model invocation capacity with Provisioned Throughput in Amazon Bedrock
Deploy a custom model for on-demand inference – Amazon Bedrock
Deploy a model in Amazon SageMaker
Right-sizing and auto-scaling an inference system – AWS Prescriptive Guidance
Skill 2.2.2: Deploy FM solutions by addressing unique challenges of LLMs that differ from traditional ML deployments (for example, by implementing container-based deployment patterns optimized for memory requirements, GPU utilization, and token processing capacity, by following specialized model loading strategies)
Optimized generative AI inference recommendations – Amazon SageMaker AI
Multi-model endpoints – Amazon SageMaker AI
Gen AI inference: architecture and best practices on AWS – AWS Prescriptive Guidance
Custom model import – Amazon Bedrock
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)
Understanding intelligent prompt routing in Amazon Bedrock
Increase throughput with cross-Region inference – Amazon Bedrock
Supported foundation models in Amazon Bedrock
Task 2.3: Design and implement enterprise integration architectures
Skill 2.3.1: Create enterprise connectivity solutions to seamlessly incorporate FM capabilities into existing enterprise environments (for example, by using API-based integrations with legacy systems, event-driven architectures to implement loose coupling, data synchronization patterns)
Amazon Bedrock AgentCore Gateway: Securely connect tools and other resources
Supported data sources for Amazon Bedrock Knowledge Bases
Skill 2.3.2: Develop integrated AI capabilities to enhance existing applications with GenAI functionality (for example, by using API Gateway to implement microservice integrations, Lambda functions for webhook handlers, Amazon EventBridge to implement event-driven integrations)
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)
Identity and access management for Amazon Bedrock
Skill 2.3.4: Develop cross-environment AI solutions to ensure data compliance across jurisdictions while enabling FM access (for example, by using AWS Outposts for on-premises data integration, AWS Wavelength to perform edge deployments, secure routing between cloud and on-premises resources)
Geographic cross-Region inference – Amazon Bedrock
Amazon Bedrock and data protection
Skill 2.3.5: Implement CI/CD pipelines and GenAI gateway architectures to implement secure and compliant consumption patterns in enterprise environments (for example, by using AWS CodePipeline, AWS CodeBuild, automated testing frameworks, centralized abstraction layers, observability and control mechanisms)
Log Amazon Bedrock API calls with AWS CloudTrail
Amazon Bedrock AgentCore Gateway: Securely connect tools and other resources
Task 2.4: Implement FM API integrations
Skill 2.4.1: Create flexible model interaction systems (for example, by using Amazon Bedrock APIs to manage synchronous requests from various compute environments, language-specific AWS SDKs and Amazon SQS for asynchronous processing, API Gateway to provide custom API clients with request validation)
Skill 2.4.2: Develop real-time AI interaction systems to provide immediate feedback from FM (for example, by using Amazon Bedrock streaming APIs for incremental response delivery, WebSockets or server-sent events to generate text in real time, API Gateway to implement chunked transfer encoding)
Stream responses with the InvokeModelWithResponseStream API – Amazon Bedrock
Get started with bidirectional streaming using WebSocket – Amazon Bedrock AgentCore
Troubleshoot issues with response streaming in API Gateway
InvokeModelWithResponseStream – Amazon Bedrock API Reference
Skill 2.4.3: Create resilient FM systems to ensure reliable operations (for example, by using the AWS SDK for exponential backoff, API Gateway to manage rate limiting, fallback mechanisms for graceful degradation, AWS X-Ray to provide observability across service boundaries)
Amazon Bedrock service quotas and throttling
Error retries and exponential backoff in AWS
Monitor Amazon Bedrock with Amazon CloudWatch
Skill 2.4.4: Develop intelligent model routing systems to optimize model selection (for example, by using application code to implement static routing configurations, Step Functions for dynamic content-based routing to specialized FMs, intelligent model routing based on metrics, API Gateway with request transformations for routing logic)
Understanding intelligent prompt routing in Amazon Bedrock
Build a prompt flow in Amazon Bedrock
AWS Step Functions Developer Guide
Task 2.5: Implement application integration patterns and development tools
Skill 2.5.1: Create FM API interfaces to address the specific requirements of GenAI workloads (for example, by using API Gateway to handle streaming responses, token limit management, retry strategies to handle model timeouts)
Amazon Bedrock service quotas and throttling
Error retries and exponential backoff in AWS
Stream responses with the InvokeModelWithResponseStream API – Amazon Bedrock
Skill 2.5.2: Develop accessible AI interfaces to accelerate adoption and integration of FMs (for example, by using AWS Amplify to develop declarative UI components, OpenAPI specifications for API-first development approaches, Amazon Bedrock Prompt Flows for no-code workflow builders)
Build a prompt flow in Amazon Bedrock
Create and design a flow in Amazon Bedrock
Skill 2.5.3: Create business system enhancements (for example, by using Lambda functions to implement CRM enhancements, Step Functions to orchestrate document processing systems, Amazon Bedrock Data Automation to manage automated data processing workflows)
Amazon Bedrock Data Automation
AWS Step Functions Developer Guide
Use action groups to define actions for Amazon Bedrock Agents
Skill 2.5.4: Enhance developer productivity to accelerate development workflows for GenAI applications (for example, by using Amazon Q Developer to generate and refactor code, code suggestions for API assistance, AI component testing, performance optimization)
Amazon Q Developer transform – modernize your code
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)
Overview – Amazon Bedrock AgentCore
Build a prompt flow in Amazon Bedrock
AWS Step Functions Developer Guide
Skill 2.5.6: Improve troubleshooting efficiency for FM applications (for example, by using CloudWatch Logs Insights to analyze prompts and responses, X-Ray to trace FM API calls, Amazon Q Developer to implement GenAI-specific error pattern recognition)
Monitor Amazon Bedrock with Amazon CloudWatch
Add observability to your Amazon Bedrock AgentCore resources
Log Amazon Bedrock API calls with AWS CloudTrail
Content Domain 3: AI Safety, Security, and Governance (20% of scored content)
Task 3.1: Implement input and output safety controls
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)
Safeguard your application with Amazon Bedrock Guardrails
Create a guardrail – Amazon Bedrock
4. Input validation and guardrails for agentic AI systems on AWS – AWS Prescriptive Guidance
AWS Step Functions Developer Guide
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, text-to-SQL transformations to ensure deterministic results)
Detect and filter harmful content by using Amazon Bedrock Guardrails
Options for handling harmful content detected by Amazon Bedrock Guardrails
Block harmful images with content filters – Amazon Bedrock
Choose the best performing model using Amazon Bedrock evaluations
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)
Use contextual grounding check to filter hallucinations in responses – Amazon Bedrock
Detect and filter harmful content by using Amazon Bedrock Guardrails
Grounding and Retrieval Augmented Generation – AWS Prescriptive Guidance
Retrieve data and generate AI responses with Amazon Bedrock Knowledge Bases
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)
4. Input validation and guardrails for agentic AI systems on AWS – AWS Prescriptive Guidance
Layer 3: Security and governance for generative AI platforms on AWS – AWS Prescriptive Guidance
Safeguard your application with Amazon Bedrock Guardrails
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)
Detect prompt attacks with Amazon Bedrock Guardrails
4. Input validation and guardrails for agentic AI systems on AWS – AWS Prescriptive Guidance
Create a guardrail – Amazon Bedrock
Task 3.2: Implement data security and privacy controls
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)
Identity and access management for Amazon Bedrock
Layer 3: Security and governance for generative AI platforms on AWS – AWS Prescriptive Guidance
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 PII, Amazon Bedrock native data privacy features, Amazon Bedrock guardrails to filter outputs, Amazon S3 Lifecycle configurations to implement data retention policies)
Remove PII from conversations by using sensitive information filters – Amazon Bedrock
Amazon Macie – Using managed data identifiers
Amazon Comprehend entity recognition
Amazon Bedrock and data protection
Setting lifecycle configuration on a bucket – Amazon S3
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)
Remove PII from conversations by using sensitive information filters – Amazon Bedrock
Amazon Comprehend PII entities detection
AWS Privacy Reference Architecture – AWS Prescriptive Guidance
Task 3.3: Implement AI governance and compliance mechanisms
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)
Implementing security and responsible AI – AWS Prescriptive Guidance
Monitor Amazon Bedrock with Amazon CloudWatch
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)
Log Amazon Bedrock API calls with AWS CloudTrail
Set up metadata and filters for your knowledge base – Amazon Bedrock
Skill 3.3.3: Create organizational governance systems to ensure consistent oversight of FM implementations (for example, by using comprehensive frameworks that align with organizational policies, regulatory requirements, and responsible AI principles)
Generative AI Lens – AWS Well-Architected Framework
Layer 3: Security and governance for generative AI platforms on AWS – AWS Prescriptive Guidance
Implementing security and responsible AI – AWS Prescriptive Guidance
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)
Monitor Amazon Bedrock with Amazon CloudWatch
Log Amazon Bedrock API calls with AWS CloudTrail
Amazon SageMaker Model Monitor
Safeguard your application with Amazon Bedrock Guardrails
Task 3.4: Implement responsible AI principles
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, Amazon Bedrock agent tracing to provide reasoning traces)
Trace the steps that Amazon Bedrock Agents takes
Monitor Amazon Bedrock with Amazon CloudWatch
Grounding and Retrieval Augmented Generation – AWS Prescriptive Guidance
Add observability to your Amazon Bedrock AgentCore resources
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 for systematic A/B testing, Amazon Bedrock with LLM-as-a-judge solutions to perform automated model evaluations)
What Is Fairness and Model Explainability for Machine Learning Predictions? – Amazon SageMaker
Pre-training Data Bias – Amazon SageMaker AI
Evaluate model performance using another LLM as a judge – Amazon Bedrock
Construct and store reusable prompts with Prompt management in Amazon Bedrock
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)
Safeguard your application with Amazon Bedrock Guardrails
Implementing security and responsible AI – AWS Prescriptive Guidance
Generative AI Lens – AWS Well-Architected Framework
Content Domain 4: Performance Optimization, Cost Management, and Observability
Task 4.1: Implement cost optimization and resource efficiency strategies
Skill 4.1.1: Develop token efficiency systems to reduce FM costs while maintaining effectiveness (for example, by using token estimation and tracking, context window optimization, response size controls, prompt compression, context pruning, response limiting)
Monitor your token usage by counting tokens before running inference – Amazon Bedrock
How tokens are counted in Amazon Bedrock
Scaling and throughput best practices – Amazon Bedrock
GENCOST03-BP03 Implement prompt caching to reduce token costs – Generative AI Lens
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)
Understanding intelligent prompt routing in Amazon Bedrock
Service tiers for optimizing performance and cost – Amazon Bedrock
Choose the best performing model using Amazon Bedrock evaluations
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)
Increase model invocation capacity with Provisioned Throughput in Amazon Bedrock
Process multiple prompts with batch inference – Amazon Bedrock
Scaling and throughput best practices – Amazon Bedrock
Monitor Amazon Bedrock with Amazon CloudWatch
Skill 4.1.4: Create intelligent caching systems to reduce costs and improve response times by avoiding unnecessary FM invocations (for example, by using semantic caching, result fingerprinting, edge caching, deterministic request hashing, prompt caching)
GENCOST03-BP03 Implement prompt caching to reduce token costs – Generative AI Lens
AI gateway capabilities in Azure API Management – Amazon API Gateway caching
Task 4.2: Optimize application performance
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)
Stream responses with the InvokeModelWithResponseStream API – Amazon Bedrock
Increase throughput with cross-Region inference – Amazon Bedrock
Scaling and throughput best practices – Amazon Bedrock
Service tiers for optimizing performance and cost – Amazon Bedrock
Skill 4.2.2: Enhance retrieval performance to improve the relevance and speed of retrieved information for FM context augmentation (for example, by using index optimization, query preprocessing, hybrid search implementation with custom scoring)
Configure retrieval and model configurations in Amazon Bedrock Knowledge Bases
Improve accuracy of model responses with reranking
Amazon OpenSearch Service vector database capabilities
Optimize performance of vector data with pgvector – AWS Prescriptive Guidance
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)
Process multiple prompts with batch inference – Amazon Bedrock
Increase model invocation capacity with Provisioned Throughput in Amazon Bedrock
Scaling and throughput best practices – Amazon Bedrock
How tokens are counted in Amazon Bedrock
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)
Inference parameters for foundation models – Amazon Bedrock
Construct and store reusable prompts with Prompt management in Amazon Bedrock
Evaluate model performance using another LLM as a judge – Amazon Bedrock
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)
Scaling and throughput best practices – Amazon Bedrock
Amazon Bedrock service quotas and throttling
Monitor Amazon Bedrock Agents using CloudWatch Metrics
Service tiers for optimizing performance and cost – Amazon Bedrock
Skill 4.2.6: Optimize FM system performance for GenAI workflows (for example, by using API call profiling for prompt-completion patterns, vector database query optimization for retrieval augmentation, latency reduction techniques specific to LLM inference, efficient service communication patterns)
Monitor Amazon Bedrock with Amazon CloudWatch
Generative AI observability – Amazon CloudWatch
Amazon OpenSearch Service vector database capabilities
Task 4.3: Implement monitoring systems for GenAI applications
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)
Generative AI observability – Amazon CloudWatch
Observe your agent applications on Amazon Bedrock AgentCore Observability
Amazon CloudWatch Dashboard User Guide
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; Amazon Bedrock Model Invocation Logs to perform detailed request and response analysis; performance benchmarks; cost anomaly detection)
Monitoring the performance of Amazon Bedrock
Model Invocations – Amazon CloudWatch
Amazon Bedrock runtime metrics
Amazon CloudWatch Anomaly Detection
AWS Cost Anomaly Detection User Guide
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)
Generative AI observability – Amazon CloudWatch
Log Amazon Bedrock API calls with AWS CloudTrail
Add observability to your Amazon Bedrock AgentCore resources
View observability data for your Amazon Bedrock AgentCore agents
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)
Amazon Bedrock AgentCore – Amazon CloudWatch
AgentCore generated runtime observability data – Amazon Bedrock AgentCore
Trace the steps that Amazon Bedrock Agents takes
Skill 4.3.5: Create vector store operational management systems to ensure optimal vector store operation and reliability for FM augmentation (for example, by using performance monitoring for vector databases, automated index optimization routines, data quality validation processes)
Monitor Amazon Bedrock Knowledge Bases with CloudWatch Logs
Sync data sources and update content – Amazon Bedrock Knowledge Bases
Amazon OpenSearch Service monitoring
Skill 4.3.6: Develop FM-specific troubleshooting frameworks to identify unique GenAI failure modes that are not present in traditional ML systems (for example, by using golden datasets to detect hallucinations, output diffing techniques to conduct response consistency analysis, reasoning path tracing to identify logical errors, specialized observability pipelines)
Observe your agent applications on Amazon Bedrock AgentCore Observability
Get started with AgentCore Observability – Amazon Bedrock AgentCore
Trace the steps that Amazon Bedrock Agents takes
Choose the best performing model using Amazon Bedrock evaluations
Content Domain 5: Testing, Validation, and Troubleshooting (11% of scored content)
Task 5.1: Implement evaluation systems for GenAI
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)
Choose the best performing model using Amazon Bedrock evaluations
Review metrics for an automated model evaluation job in Amazon Bedrock
Evaluate model performance using another LLM as a judge – Amazon Bedrock
Generative AI quality metrics – Generative AI Lens
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 to measure token efficiency, latency-to-quality ratios, and business outcomes)
Choose the best performing model using Amazon Bedrock evaluations
Create an evaluation job – Amazon Bedrock
Evaluate model performance using another LLM as a judge – Amazon Bedrock
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)
Amazon Augmented AI (Amazon A2I) Developer Guide
Create a human evaluation job – Amazon Bedrock
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)
Create an evaluation job – Amazon Bedrock
Monitor Amazon Bedrock with Amazon CloudWatch
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)
Evaluate model performance using another LLM as a judge – Amazon Bedrock
RAG evaluation for Amazon Bedrock Knowledge Bases
Create a human evaluation job – Amazon Bedrock
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)
RAG evaluation for Amazon Bedrock Knowledge Bases
Configure retrieval and model configurations in Amazon Bedrock Knowledge Bases
Improve accuracy of model responses with reranking
Monitor Amazon Bedrock Knowledge Bases with CloudWatch Logs
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)
Evaluators – Amazon Bedrock AgentCore
Monitor Amazon Bedrock Agents using CloudWatch Metrics
Trace the steps that Amazon Bedrock Agents takes
Observe your agent applications on Amazon Bedrock AgentCore Observability
Skill 5.1.8: Create comprehensive reporting systems to communicate performance metrics and insights effectively to stakeholders for FM implementations (for example, by using visualization tools, automated reporting mechanisms, model comparison visualizations)
Amazon CloudWatch Dashboard User Guide
Generative AI observability – Amazon CloudWatch
View observability data for your Amazon Bedrock AgentCore agents
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)
Use contextual grounding check to filter hallucinations in responses – Amazon Bedrock
Amazon SageMaker Model Monitor
Create an evaluation job – Amazon Bedrock
Task 5.2: Troubleshoot GenAI applications
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)
Configure chunking for Amazon Bedrock Knowledge Bases
Monitor your token usage by counting tokens before running inference – Amazon Bedrock
Prompt engineering concepts – Amazon Bedrock
Scaling and throughput best practices – Amazon Bedrock
Skill 5.2.2: Diagnose and resolve FM integration issues to identify and fix API integration problems specific to GenAI services (for example, by using error logging, request validation, response analysis)
Monitoring the performance of Amazon Bedrock
Amazon Bedrock service quotas and throttling
Error retries and exponential backoff in AWS
Log Amazon Bedrock API calls with AWS CloudTrail
Skill 5.2.3: Troubleshoot prompt engineering problems to improve FM response quality and consistency beyond basic prompt adjustments (for example, by using prompt testing frameworks, version comparison, systematic refinement)
Test a prompt using Prompt management – Amazon Bedrock
Manage versions of your prompt – Amazon Bedrock
Advanced prompt templates – Amazon Bedrock
Construct and store reusable prompts with Prompt management in Amazon Bedrock
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)
Sync data sources and update content – Amazon Bedrock Knowledge Bases
Monitor Amazon Bedrock Knowledge Bases with CloudWatch Logs
RAG evaluation for Amazon Bedrock Knowledge Bases
Configure chunking for Amazon Bedrock Knowledge Bases
Skill 5.2.5: Troubleshoot prompt maintenance issues to continuously improve the performance of FM interactions (for example, by using template testing and CloudWatch Logs to diagnose prompt confusion, X-Ray to implement prompt observability pipelines, schema validation to detect format inconsistencies, systematic prompt refinement workflows)
Monitor Amazon Bedrock with Amazon CloudWatch
Amazon Bedrock AgentCore and AWS X-Ray
Troubleshoot AgentCore Runtime – Amazon Bedrock AgentCore
Diagnose AgentCore Evaluation issues with an AI coding assistant – Amazon Bedrock AgentCore
Test a prompt using Prompt management – Amazon Bedrock
This brings us to the end of the AIP-C01 AWS Certified Generative AI Developer Professional exam study guide.
What do you think? Let me know in the comments section if I have missed out on anything. Also, I love to hear from you how your preparation is going on!
In case you are preparing for other AWS certification exams, check out the AWS study guides for those exams.
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