AWS Certified Generative AI Developer - Professional
This course prepares experienced developers for the AWS Certified Generative AI Developer - Professional exam. Participants study how to build and deploy generative AI applications on AWS using Amazon Bedrock, retrieval-augmented generation, agents, security controls, monitoring, and cost management.
No sessions available
Check back later or contact a provider directly.
Description
This course prepares experienced developers for the AWS Certified Generative AI Developer - Professional exam.
Participants study how to build and deploy generative AI applications on AWS using Amazon Bedrock, retrieval-augmented generation, agents, security controls, monitoring, and cost management.
Teaching combines instructor-led explanations, guided AWS labs, case studies, and exam-style questions. The course balances architecture concepts with practical implementation, including identity management, infrastructure as code, and observability.
After completion, participants can plan production-grade GenAI solutions and assess readiness for the AIP-C01 certification exam.
What You Will Learn
Foundation model solution design
Review the scope of the AWS Certified Generative AI Developer, Professional exam and its focus on production generative AI applications on AWS.
Compare foundation model selection, Amazon Bedrock usage patterns, Amazon SageMaker AI endpoints, deployment options, and model lifecycle needs.
Plan Retrieval Augmented Generation, RAG, solutions using embeddings, vector stores, Amazon Bedrock Knowledge Bases, Amazon OpenSearch Service, Amazon S3, and metadata strategies.
Data preparation, prompts, and application integration
Prepare text, image, audio, and tabular inputs for foundation model consumption using AWS Glue Data Quality, SageMaker Data Wrangler, AWS Lambda, Amazon Transcribe, and Amazon Comprehend.
Apply prompt engineering, prompt templates, Amazon Bedrock Prompt Management, Amazon Bedrock Prompt Flows, prompt testing, and version control.
Build API and event-driven integration patterns with Amazon API Gateway, AWS Lambda, Amazon SQS, Amazon EventBridge, AWS Step Functions, and AWS SDKs.
Agentic AI, security, and governance
Study agent workflows, tool use, memory and state management, Model Context Protocol, human review steps, and stopping conditions.
Implement content safety, prompt injection controls, Amazon Bedrock Guardrails, IAM least privilege, VPC endpoints, AWS KMS, Amazon Macie, and CloudTrail audit logging.
Address responsible AI practices, data lineage, model documentation, compliance checks, and governance controls for enterprise use.
Operations, evaluation, and troubleshooting
Monitor token usage, latency, cost, response quality, hallucination rates, and vector store performance with Amazon CloudWatch, CloudWatch Logs, AWS X-Ray, and Amazon Bedrock Model Invocation Logs.
Use A/B testing, canary testing, Amazon Bedrock Model Evaluations, golden datasets, retrieval testing, and agent performance measures.
Diagnose context window overflow, API errors, prompt regressions, embedding drift, retrieval failures, and response inconsistency.
Certification & Exam
This course prepares participants for the AWS Certified Generative AI Developer, Professional certification exam, exam code AIP-C01.
The exam is a professional-level assessment delivered through Pearson VUE, either at a testing center or as an online proctored exam.
It lasts 180 minutes and includes 75 multiple-choice or multiple-response questions. Of these, 65 questions are scored and 10 are unscored. Results are reported on a scaled score from 100 to 1,000, with a minimum passing score of 750.
The exam validates skills in integrating foundation models, using RAG and vector stores, applying prompt techniques, building agentic AI solutions, and managing security, governance, monitoring, and optimization for generative AI applications on AWS. Reference: official AWS certification page and AWS exam guide.
What You Will Achieve
After completing this course, participants will be able to:
Analyze business and technical requirements for generative AI applications, then map them to suitable foundation models, AWS services, deployment patterns, and integration options.
Design Retrieval Augmented Generation solutions using vector stores, embeddings, document chunking, metadata, Amazon Bedrock Knowledge Bases, Amazon OpenSearch Service, Amazon Aurora with pgvector, and related retrieval methods.
Implement foundation model integrations in applications and workflows using Amazon Bedrock APIs, AWS Lambda, Amazon API Gateway, AWS Step Functions, Amazon SQS, streaming responses, retry logic, and rate limiting controls.
Create prompt engineering and prompt management approaches that support consistent model behavior, reusable templates, approval workflows, prompt testing, Amazon Bedrock Prompt Management, and Amazon Bedrock Prompt Flows.
Build agentic AI solutions with tool use, memory, state management, multi-step reasoning workflows, stopping conditions, human review steps, and controlled access to external systems.
Apply security, privacy, governance, and Responsible AI controls using IAM, VPC endpoints, Amazon Bedrock Guardrails, AWS CloudTrail, Amazon CloudWatch, Amazon Comprehend, Amazon Macie, audit logging, PII detection, and policy-based output filtering.
Optimize generative AI applications for cost, latency, throughput, and response quality through model selection, token tracking, caching, batching, provisioned throughput, context window management, and performance testing.
Evaluate and troubleshoot generative AI systems using Amazon Bedrock Model Evaluations, A/B testing, canary testing, RAG evaluation, hallucination checks, regression testing, CloudWatch Logs, AWS X-Ray, and retrieval quality diagnostics.
Training Providers
No providers available for this course yet.
FAQs
General Information
Prerequisites & Requirements
Certification & Exam
Get Custom In-house Training
Post once, get competitive offers from multiple providers. Choose the one that fits your team.
Similar Trainings
AWS Certified Cloud Practitioner
This provider-led AWS Certified Cloud Practitioner course introduces core AWS Cloud concepts for business, technical, sales, and support roles. Participants study AWS global infrastructure, IAM, EC2, S3, VPC, Lambda, security responsibilities, pricing, billing, and support plans. Teaching may include instructor-led lessons, service demos, knowledge checks, case studies, and practical exercises focused on real workplace scenarios. After completion, participants should be able to explain key AWS services, discuss basic cloud value, identify security and cost considerations, and prepare for the AWS Certified Cloud Practitioner exam.
Network+
CompTIA Network+ is the premier certification for validating your knowledge of essential networking tools and concepts. You will be assessed on your abilities in network connectivity, documentation, service configuration, data centers, cloud, virtual networking, monitoring, troubleshooting, and security hardening. This certification prepares you for jobs in technical support, network operations, and system administration.
AWS Certified Machine Learning Engineer - Associate
This course covers AWS machine learning workflows for the AWS Certified Machine Learning Engineer, Associate certification. Participants study data preparation, feature engineering, model training, evaluation, deployment, monitoring, and responsible AI practices. Teaching typically combines lectures, demonstrations, hands-on labs, and case studies using AWS services such as Amazon SageMaker , Amazon S3 , AWS Glue , and AWS Lambda . The course balances core ML concepts with practical AWS tasks, helping participants prepare for the exam and apply machine learning workflows in cloud-based projects.
AWS Certified Solutions Architect - Associate
Prepare for the SAA-C03 exam through guided lectures, hands-on AWS labs , architecture diagrams, and scenario-based case studies. Participants study the AWS Well-Architected Framework, IAM, VPC networking, EC2, S3, RDS, Lambda, Route 53, CloudFront, Auto Scaling, Elastic Load Balancing, and CloudWatch. The course balances exam theory with design practice for secure, resilient, high-performing, and cost-optimized architectures. After completion, learners can assess requirements, choose suitable AWS services, review existing designs, and plan next steps toward AWS certification.
AWS Certified Data Engineer - Associate
This course covers data engineering on AWS, with a focus on building, operating, and maintaining data pipelines using AWS Glue, Amazon Redshift, Amazon Kinesis, Amazon S3, and related AWS services. Training typically combines lectures, hands-on labs, and scenario-based exercises to connect core concepts with practical implementation. Participants study data ingestion, transformation, orchestration, storage, monitoring, security, and cost-aware design. After completion, they can plan and support AWS data workflows and prepare for the AWS Certified Data Engineer Associate certification exam.
AWS Certified Developer - Associate
This course covers cloud application development for the AWS Certified Developer Associate certification, with a focus on AWS Lambda, DynamoDB, API Gateway, AWS SDKs, IAM, and CI/CD workflows. Participants learn how to build, test, deploy, and monitor serverless applications, manage application data, use authentication and permissions, and troubleshoot common issues. Teaching may include instructor-led lectures, hands-on labs, demos, and exam-style practice questions. The content balances AWS service concepts with practical development tasks, helping developers and technical teams prepare for certification and apply AWS development skills at work.