AWS Certified Generative AI Developer Professional Quick Facts (2025)

Exam Domain Breakdown

Domain 1: Content Domain 1: Foundation Model Integration, Data Management, and Compliance (31% of the exam)

Task 1.1: Analyze requirements and design GenAI solutions.

• Skills: Create architectures aligned with business needs, build POCs using Amazon Bedrock, and standardize components via the Well-Architected Framework and Generative AI Lens.

Task 1.1 summary: This task focuses on shaping generative AI architectures that align technical decisions with measurable business outcomes. You will need to evaluate constraints, select the right foundation models (FMs), and demonstrate how proof-of-concept implementations validate both performance and value before scaling. The emphasis is on structured design approaches that use AWS’s best practices for scalability, performance, and reliability.

Understanding the Well-Architected Framework and the AWS WA Tool Generative AI Lens is key to ensuring that implemented patterns are standardized and consistent across multiple environments. This section ensures that you not only design strong architectures but also document and reuse technical components that streamline future generative AI deployments.

Task 1.2: Select and configure FMs.

• Skills: Choose FMs aligned with the use case, build flexible switching architectures, ensure resilience, and manage the full FM lifecycle.

Task 1.2 summary: This task develops your ability to select and configure foundation models based on functional fit, business value, and technical benchmarks. You’ll learn to architect flexible systems that allow switching between providers without refactoring code, supporting agility and maintainability across evolving models and APIs.

You’ll also work on designing systems for resilience in distributed deployments through multiregional and automated recovery patterns. Understanding FM customization techniques, using SageMaker for fine-tuning, managing lifecycle states, and applying version control helps ensure continuous, reliable improvements to generative AI capabilities in production environments.

Task 1.3: Implement data validation and processing pipelines for FM consumption.

• Skills: Validate and process structured and unstructured data for FM use with AWS Glue, SageMaker, Lambda, and Bedrock APIs.

Task 1.3 summary: This section ensures that you can create robust data validation systems for preparing inputs to generative AI models. Designing workflows for data quality and schema conformance ensures smooth integration with FMs, leveraging automation through AWS Glue Data Quality and CloudWatch metrics.

You’ll also build data pipelines that translate complex multimodal information—such as text, image, or audio—into formats optimized for high-quality FM inference. By implementing standardization, enrichment, and reformatting pipelines, you’ll enhance model accuracy and ensure that every input supports the FM’s contextual understanding and performance goals.

Task 1.4: Design and implement vector store solutions.

• Skills: Build high-performance vector databases and metadata frameworks using OpenSearch, DynamoDB, and Bedrock Knowledge Bases.

Task 1.4 summary: This task develops the expertise required to design resilient, large-scale vector stores for generative AI applications. You’ll implement advanced indexing, sharding, and organization strategies that improve semantic retrieval accuracy. Understanding how to pair vector databases with metadata enhances both discoverability and contextual performance for generative models.

The focus is on ensuring precision, speed, and maintainability in real-world conditions. You’ll also explore techniques for managing vector store freshness through automated updates and dynamic synchronization workflows, ensuring that retrieved knowledge is always current and relevant to AI interactions.

Task 1.5: Design retrieval mechanisms for FM augmentation.

• Skills: Segment documents for optimal retrieval, configure embeddings, deploy semantic search architectures, and manage hybrid query systems.

Task 1.5 summary: This section covers creating sophisticated retrieval systems that improve how FMs use contextual information. You’ll implement methods like chunking and vector embedding generation to break down and store data effectively. The goal is to enhance FMs with accurate and contextual retrieval capabilities that strengthen the quality of generative responses.

You’ll also focus on designing hybrid search systems that combine keyword and semantics-based results, improving recall and response relevance. Mastering structured query handling and consistent access mechanisms ensures that vector retrieval capabilities are integrated cleanly into generative AI pipelines.

Task 1.6: Implement prompt engineering strategies and governance for FM interactions.

• Skills: Design robust prompting frameworks, build interactive AI systems, and establish prompt governance using Bedrock Guardrails and Prompt Management.

Task 1.6 summary: This task ensures mastery of prompt engineering as both an art and a disciplined practice. You’ll design structured prompt templates that deliver consistent results across complex AI interactions, including role assignment and contextual management. Governance features like Bedrock Prompt Management and Guardrails strengthen oversight for responsible and traceable output generation.

Additionally, you’ll develop patterns for prompt testing, refinement, and contextual optimization. Implementing scalable prompt management processes ensures long-term quality assurance while enabling rapid iteration for enhanced model reliability and performance.

Domain 2: Content Domain 2: Implementation and Integration (26% of the exam)

Task 2.1: Implement agentic AI solutions and tool integrations.

• Skills: Develop agent-based systems, implement tool integrations, and manage secure multi-agent workflows.

Task 2.1 summary: This task explores how to extend FMs with agentic behaviors that automate complex reasoning and decision-making tasks. You’ll use technologies like AWS Agent Squad, Strands Agents, and Step Functions to build multi-agent systems that simulate collaborative intelligence patterns within enterprise systems.

You’ll implement integrations for external tools through standard interfaces, improving reliability, observability, and safety in execution. Emphasis is placed on combining automation with human-in-the-loop design for balanced and adaptive AI-driven operations.

Task 2.2: Implement model deployment strategies.

• Skills: Deploy FMs for performance and scalability using Lambda, Bedrock, and SageMaker endpoints.

Task 2.2 summary: This task teaches advanced deployment approaches that balance computation, memory, and throughput requirements unique to foundation models. You’ll configure APIs and endpoints optimized for FM-specific workloads, using provisioned throughput and resource isolation strategies to ensure consistent performance.

Key insights include handling container configurations, scaling for GPU utilization, and leveraging asynchronous or hybrid architectures to increase cost efficiency. You’ll master the processes for monitoring and iterating deployments to maintain continuous improvement.

Task 2.3: Design and implement enterprise integration architectures.

• Skills: Integrate FMs with enterprise systems, secure data movement, and enforce compliant access control.

Task 2.3 summary: This task emphasizes building connection-ready architectures that bring FMs into existing enterprise ecosystems. You’ll create API-driven, event-based models that follow loose coupling and fault-tolerant integration patterns through AWS services like EventBridge and API Gateway.

You’ll also design secure access mechanisms that respect compliance, data privacy, and governance requirements. Integrating CI/CD pipelines for continuous updates ensures stable, governed AI deployment across distributed enterprise environments.

Task 2.4: Implement FM API integrations.

• Skills: Develop resilient, scalable FM interaction APIs with robust request management and observability.

Task 2.4 summary: The focus here is on implementing scalable and fault-tolerant ways for applications to communicate with FMs using API Gateway, Amazon SQS, and Bedrock SDKs. You’ll design systems that balance synchronous and asynchronous interaction models to optimize performance for various workloads.

Enhanced patterns such as streaming inference, backoff handling, and dynamic routing ensure that applications stay responsive and resilient. The result is a flexible, reliable integration layer that drives performance in production environments.

Task 2.5: Implement application integration patterns and development tools.

• Skills: Build GenAI APIs, develop tools with Amazon Q Developer and Amplify, and optimize end-to-end workflows.

Task 2.5 summary: In this section, you’ll design generative AI interfaces that accelerate adoption by blending intuitive usability with technical precision. Whether building toolkits for developers or responsive UIs through AWS Amplify, you will streamline how teams create, integrate, and maintain GenAI experiences.

You’ll also develop repeatable patterns that enhance productivity, automate testing, and leverage AI-assisted development tools. Understanding best practices for integrating advanced agents and orchestrations ensures that generative systems evolve with ease and accountability.

Domain 3: Content Domain 3: AI Safety, Security, and Governance (20% of the exam)

Task 3.1: Implement input and output safety controls.

• Skills: Build content moderation systems, prevent harmful outputs, and detect adversarial inputs.

Task 3.1 summary: This domain prioritizes responsible AI by ensuring all model interactions are secure, ethical, and policy aligned. You’ll design layered moderation systems with AWS Bedrock guardrails, custom workflows, and validation mechanisms to maintain quality and safety.

Using structured verifications and continuous monitoring, you’ll protect both data integrity and user trust. Special focus is placed on detecting, filtering, and mitigating issues like adversarial prompting or unsafe outputs before they reach users.

Task 3.2: Implement data security and privacy controls.

• Skills: Secure FM environments with IAM, VPC isolation, and privacy tools like Comprehend and Macie.

Task 3.2 summary: This task ensures mastery of protecting sensitive information while still enabling high-performing generative models. You’ll set up private network access, encryption, and fine-grained permission controls using AWS services designed for confidentiality and data protection.

Beyond preventing unauthorized access, you’ll also ensure compliance through automated privacy scanning and retention management. The goal is to achieve comprehensive, policy-aligned data privacy that empowers innovation without compromise.

Task 3.3: Implement AI governance and compliance mechanisms.

• Skills: Build frameworks for auditable, transparent AI systems with continuous compliance monitoring.

Task 3.3 summary: This section reinforces how to create trustworthy AI ecosystems that align with organizational governance and regulations. Implementing traceability and data lineage ensures accountability across the entire AI lifecycle while meeting global compliance standards.

You’ll design monitoring loops for detecting bias, misuse, or drift, coupled with automated alerting and remediation to sustain compliance maturity. Governance focuses on making AI operations secure, explainable, and transparent.

Task 3.4: Implement responsible AI principles.

• Skills: Ensure fairness, accountability, and explainability through transparent design and evaluation frameworks.

Task 3.4 summary: This task focuses on creating AI systems that demonstrate integrity, fairness, and accountability in every output. By designing transparent interfaces and leveraging Bedrock guardrails, you’ll ensure that users understand model reasoning and confidence.

You’ll also use continuous evaluations to detect bias and maintain equitable outcomes. Integrating responsible AI tools into the development lifecycle establishes scalable processes that align innovation with social responsibility.

Domain 4: Content Domain 4: Operational Efficiency and Optimization for GenAI Applications (12% of the exam)

Task 4.1: Implement cost optimization and resource efficiency strategies.

• Skills: Reduce FM costs via token optimization, caching, and scaling strategies.

Task 4.1 summary: This domain trains you to build GenAI applications that maximize performance per cost unit. You’ll analyze token usage patterns, implement semantic caching, and rightsize inference workloads for efficiency without reducing model quality.

In addition to performance optimization, you’ll explore dynamic scaling and capacity planning techniques to achieve predictable operation patterns while minimizing excess consumption. The result is a finely tuned AI environment that sustains innovation and cost-effectiveness together.

Task 4.2: Optimize application performance.

• Skills: Improve latency, throughput, and retrieval performance through advanced tuning and benchmarking.

Task 4.2 summary: This task ensures that you can elevate performance across all layers of a GenAI application. By leveraging pre-computation, streaming responses, and latency optimization, you’ll enhance the responsiveness and user experience of real-time AI systems.

You’ll analyze token-level performance and benchmark retrieval mechanisms to achieve both speed and precision. These optimizations ensure that your FMs remain efficient, scalable, and ready to meet dynamic business demands.

Task 4.3: Implement monitoring systems for GenAI applications.

• Skills: Deploy observability systems for performance, reliability, and compliance monitoring across AI workloads.

Task 4.3 summary: You’ll design comprehensive monitoring frameworks tailored for FM-based architectures using CloudWatch, X-Ray, and Bedrock logs. The goal is to gain full visibility into token usage, latency trends, and hallucination metrics in production environments.

Through integrated dashboards and automated anomaly detection, you’ll maintain continuous insight into behavior, ensuring applications remain both performant and compliant. Observability becomes the foundation for proactive optimization and long-term solution resilience.

Domain 5: Content Domain 5: Testing, Validation, and Troubleshooting (11% of the exam)

Task 5.1: Implement evaluation systems for GenAI.

• Skills: Build model evaluation, A/B testing, and quality assurance pipelines for FM performance verification.

Task 5.1 summary: This task covers designing evaluation frameworks that measure model quality using relevance, accuracy, and consistency metrics. You’ll implement workflows for model comparison and automated validation to capture performance shifts early and continuously.

Embedding quality assurance practices into the deployment cycle ensures that FMs uphold desired performance metrics post-deployment. By incorporating user feedback and structured assessments, you sustain both innovation and reliability over time.

Task 5.2: Troubleshoot GenAI applications.

• Skills: Diagnose FM performance, prompt quality, retrieval processes, and integration issues with systematic approaches.

Task 5.2 summary: This section enhances your troubleshooting skills for every stage of a generative AI lifecycle. You’ll learn structured methods to resolve context handling, API, and embedding-related challenges through proactive monitoring and analytics.

You’ll deploy observability tools like CloudWatch and X-Ray to identify prompt inconsistencies, format errors, or vector retrieval issues. This ensures every component, from prompt templates to semantic retrieval, performs seamlessly for predictable AI outcomes.