AWS aws-certification documentation change
Summary
Restructured skills documentation from headings to bullet points and added new skills across domains including architectural design, FM customization, data processing, vector databases, and prompt management.
Security assessment
Added Skill 1.6.1 explicitly documents Amazon Bedrock Guardrails for enforcing responsible AI guidelines, which is a security feature. Skill 1.6.3 adds documentation for prompt governance using CloudTrail for usage tracking, enhancing security oversight. No evidence of addressing specific vulnerabilities.
Diff
diff --git a/aws-certification/latest/examguides/ai-professional-01-domain1.md b/aws-certification/latest/examguides/ai-professional-01-domain1.md index ffa8b4cf0..79d887e2c 100644 --- a//aws-certification/latest/examguides/ai-professional-01-domain1.md +++ b//aws-certification/latest/examguides/ai-professional-01-domain1.md @@ -11 +11,6 @@ Task 1.1: Analyze requirements and design GenAI solutions.Task 1.2: Select and c -### 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). + * 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). + + * 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). + + * 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). + @@ -13 +17,0 @@ Task 1.1: Analyze requirements and design GenAI solutions.Task 1.2: Select and c -### 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). @@ -15 +18,0 @@ Task 1.1: Analyze requirements and design GenAI solutions.Task 1.2: Select and c -### 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). @@ -19 +22,7 @@ Task 1.1: Analyze requirements and design GenAI solutions.Task 1.2: Select and c -### 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). + * 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). + + * 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). + + * 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). + + * 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). @@ -21 +29,0 @@ Task 1.1: Analyze requirements and design GenAI solutions.Task 1.2: Select and c -### 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). @@ -23 +30,0 @@ Task 1.1: Analyze requirements and design GenAI solutions.Task 1.2: Select and c -### 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). @@ -25 +31,0 @@ Task 1.1: Analyze requirements and design GenAI solutions.Task 1.2: Select and c -### 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). @@ -29 +35,7 @@ Task 1.1: Analyze requirements and design GenAI solutions.Task 1.2: Select and c -### 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). + * 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). + + * 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). + + * 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). + + * 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). @@ -31 +42,0 @@ Task 1.1: Analyze requirements and design GenAI solutions.Task 1.2: Select and c -### 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). @@ -33 +43,0 @@ Task 1.1: Analyze requirements and design GenAI solutions.Task 1.2: Select and c -### 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). @@ -35 +44,0 @@ Task 1.1: Analyze requirements and design GenAI solutions.Task 1.2: Select and c -### 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). @@ -39 +48 @@ Task 1.1: Analyze requirements and design GenAI solutions.Task 1.2: Select and c -### 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). + * 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). @@ -41 +50,7 @@ Task 1.1: Analyze requirements and design GenAI solutions.Task 1.2: Select and c -### 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). + * 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). + + * 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). + + * 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). + + * 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). @@ -43 +57,0 @@ Task 1.1: Analyze requirements and design GenAI solutions.Task 1.2: Select and c -### 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). @@ -45 +58,0 @@ Task 1.1: Analyze requirements and design GenAI solutions.Task 1.2: Select and c -### 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). @@ -47 +59,0 @@ Task 1.1: Analyze requirements and design GenAI solutions.Task 1.2: Select and c -### 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). @@ -51 +63,5 @@ Task 1.1: Analyze requirements and design GenAI solutions.Task 1.2: Select and c -### 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). + * 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). + + * 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). + + * 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). @@ -53 +69 @@ Task 1.1: Analyze requirements and design GenAI solutions.Task 1.2: Select and c -### 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). + * 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). @@ -55 +71,3 @@ Task 1.1: Analyze requirements and design GenAI solutions.Task 1.2: Select and c -### 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). + * 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). + + * 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). @@ -57 +74,0 @@ Task 1.1: Analyze requirements and design GenAI solutions.Task 1.2: Select and c -### 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). @@ -59 +75,0 @@ Task 1.1: Analyze requirements and design GenAI solutions.Task 1.2: Select and c -### 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). @@ -61 +76,0 @@ Task 1.1: Analyze requirements and design GenAI solutions.Task 1.2: Select and c -### 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). @@ -65 +80,7 @@ Task 1.1: Analyze requirements and design GenAI solutions.Task 1.2: Select and c -### 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). + * 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). + + * 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). + + * 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). + + * 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). @@ -67 +88 @@ Task 1.1: Analyze requirements and design GenAI solutions.Task 1.2: Select and c -### 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). + * 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). @@ -69 +90 @@ Task 1.1: Analyze requirements and design GenAI solutions.Task 1.2: Select and c -### 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). + * 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). @@ -71 +91,0 @@ Task 1.1: Analyze requirements and design GenAI solutions.Task 1.2: Select and c -### 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). @@ -73 +92,0 @@ Task 1.1: Analyze requirements and design GenAI solutions.Task 1.2: Select and c -### 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). @@ -75 +93,0 @@ Task 1.1: Analyze requirements and design GenAI solutions.Task 1.2: Select and c -### 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).