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AWS Certified Generative AI Developer - Professional
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Course Description
Overview
The AWS Certified Generative AI Developer - Professional (AIP-C01) exam is intended for individuals who perform a GenAI developer role. The exam validates a candidate's ability to effectively integrate foundation models (FMs) into applications and business workflows. This certification demonstrates practical knowledge of how to implement GenAI solutions into production environments by using AWS technologies.The exam also validates a candidate's ability to complete the following tasks:
- Design and implement solutions by using vector stores, Retrieval Augmented Generation (RAG), knowledge bases, and other GenAI architectures.
- Integrate FMs into applications and business workflows.
- Apply prompt engineering and management techniques.
- Implement agentic AI solutions.
- Optimize GenAI applications for cost, performance, and business value.
- Implement security, governance, and Responsible AI practices.
- Troubleshoot, monitor, and optimize GenAI applications.
- Evaluate FMs for quality and responsibility.
Objectives
Audience
Prerequisites
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The target candidate should have the following AWS knowledge:
- Experience with AWS compute, storage, and networking services
- Understanding of AWS security best practices and identity management
- Experience with AWS deployment and infrastructure as code (IaC) tools
- Familiarity with AWS monitoring and observability services
- Understanding of AWS cost optimization principles
Topics
- Task 1.1: Analyze requirements and design GenAI solutions.
- Task 1.2: Select and configure FMs.
- Task 1.3: Implement data validation and processing pipelines for FM consumption.
- Task 1.4: Design and implement vector store solutions.
- Task 1.5: Design retrieval mechanisms for FM augmentation.
- Task 1.6: Implement prompt engineering strategies and governance for FM interactions.
- Task 2.1: Implement agentic AI solutions and tool integrations.
- Task 2.2: Implement model deployment strategies.
- Task 2.3: Design and implement enterprise integration architectures.
- Task 2.4: Implement FM API integrations.
- Task 2.5: Implement application integration patterns and development tools.
- Task 3.1: Implement input and output safety controls.
- Task 3.2: Implement data security and privacy controls.
- Task 3.3: Implement AI governance and compliance mechanisms.
- Task 3.4: Implement responsible AI principles.
- Task 4.1: Implement cost optimization and resource efficiency strategies.
- Task 4.2: Optimize application performance.
- Task 4.3: Implement monitoring systems for GenAI applications.
- Task 5.1: Implement evaluation systems for GenAI.
- Task 5.2: Troubleshoot GenAI applications.
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Self-Paced Training Info
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Self-Paced Training Terms & Conditions
THIS IS A SELF-PACED VIRTUAL CLASS. AFTER YOU REGISTER, YOU HAVE 30 DAYS TO COMPLETE THE COURSE.
Before you enroll, review the system requirements to ensure that your system meets the minimum requirements for this course. AFTER YOU ARE ENROLLED IN THIS COURSE, YOU WILL NOT BE ABLE TO CANCEL YOUR ENROLLMENT. You are billed for the course when you submit the enrollment form. Self-Paced Virtual Classes are non-refundable. Once you purchase a Self-Paced Virtual Class, you will be charged the full price.
After you receive confirmation that you are enrolled, you will be sent further instructions to access your course material and remote labs. A confirmation email will contain your online link, your ID and password, and additional instructions for starting the course.
You can start the course at any time within 12 months of enrolling for the course. After you register/start the course, you have 30 days to complete your course. Within this 30 days, the self-paced format gives you the opportunity to complete the course at your convenience, at any location, and at your own pace. The course is available 24 hours a day.
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