AWS Certified Generative AI Developer Professional Exams

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Test your skills with 4 AWS Generative AI Developer Professional–level practice exams.
⭐ 3.70/5 rating
πŸ‘₯ 183 students
πŸ”„ November 2025 update

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  • Course Overview

    • Purpose-Built Preparation: Offers unparalleled, focused preparation for the challenging AWS Certified Generative AI Developer Professional certification exam. This course is meticulously designed to equip candidates with the confidence and knowledge required to succeed in a rapidly evolving technological domain.
    • Realistic Practice: Features 4 full-length, professional-level practice examinations, each meticulously simulating the actual exam’s format, difficulty, and question types. This hands-on practice environment is crucial for familiarizing learners with the pressure and structure of the real certification test.
    • Up-to-Date Content: Thoroughly updated to reflect the latest AWS services, features, and examination objectives as of November 2025. This commitment to currency ensures that learners are preparing with the most relevant and accurate information available, a critical factor in the dynamic field of generative AI.
    • Targeted Audience: Ideal for experienced developers, machine learning engineers, and solutions architects aiming to validate their advanced expertise in building, deploying, and optimizing cutting-edge generative AI solutions on the AWS cloud platform. It caters to those ready to specialize.
    • Skill Validation & Gap Analysis: Specifically designed as an essential tool to identify knowledge gaps, solidify understanding across all essential exam domains, and enhance overall readiness and confidence before attempting the official AWS certification.

  • Requirements / Prerequisites

    • Solid AWS Foundation: Proficient understanding of core AWS services, including compute (AWS Lambda, Amazon EC2), storage (Amazon S3), databases (Amazon DynamoDB), networking, AWS Identity and Access Management (IAM), and Infrastructure as Code using AWS CloudFormation.
    • Generative AI Core Concepts: Strong grasp of fundamental generative AI concepts, such as large language models (LLMs), foundation models (FMs), prompt engineering techniques, few-shot learning, Retrieval-Augmented Generation (RAG), and model fine-tuning.
    • Python Proficiency: Demonstrated programming skills in Python are mandatory, coupled with practical experience using relevant AI/ML libraries and AWS SDKs (Boto3) for programmatic interaction with AWS services and generative AI models.
    • Hands-on AWS Generative AI Experience: Prior practical experience in developing, deploying, and managing generative AI applications on the AWS platform, specifically utilizing services like Amazon Bedrock and Amazon SageMaker, is highly recommended.
    • MLOps Familiarity (Recommended): A basic understanding of Machine Learning Operations (MLOps) principles on AWS, including model deployment strategies, versioning, and continuous monitoring, would be highly beneficial for exam success.

  • Skills Covered / Tools Used

    • Generative AI Fundamentals on AWS: Deep understanding of various foundation models (FMs) available through Amazon Bedrock, encompassing text, image, and code generation, along with their respective capabilities and suitable use cases.
    • Advanced Prompt Engineering: Mastering the art of crafting effective and optimized prompts, utilizing advanced techniques like prompt chaining, few-shot learning, and Chain-of-Thought (CoT) to elicit precise outputs from FMs.
    • Model Customization & Adaptation: Strategies for adapting pre-trained foundation models through techniques such as full fine-tuning, Parameter-Efficient Fine-Tuning (PEFT) methods (e.g., LoRA), and implementing Retrieval-Augmented Generation (RAG) architectures with Amazon Bedrock and Amazon SageMaker.
    • Application Development with Bedrock & SageMaker: Building robust, full-stack generative AI applications using AWS SDKs, integrating Amazon Bedrock with AWS Lambda, Amazon API Gateway, Amazon S3, and Amazon DynamoDB, plus leveraging SageMaker for custom model tasks.
    • Data Preparation & Management for Generative AI: Sourcing, preprocessing, and transforming diverse datasets suitable for model training, evaluation, and populating knowledge bases for RAG systems using AWS Glue, Amazon S3, and Amazon OpenSearch Service for vector embeddings.
    • Evaluation & Monitoring of Generative AI Models: Implementing robust metrics and methodologies for quantitatively and qualitatively evaluating model performance, identifying biases, ensuring fairness, and establishing continuous monitoring pipelines using Amazon CloudWatch and Amazon SageMaker Model Monitor.
    • Security & Governance in Generative AI: Applying industry best practices for securing generative AI applications and sensitive data on AWS, including fine-grained access control with IAM roles, data encryption, ensuring data governance, and adhering to responsible AI principles.
    • Deployment & MLOps for Generative AI: Orchestrating efficient model deployment workflows, setting up continuous integration and continuous delivery (CI/CD) pipelines, managing model versions, and ensuring scalable, resilient generative AI solutions using Amazon SageMaker MLOps, Amazon ECS, and Amazon EKS.
    • Cost Optimization for Generative AI Workloads: Understanding the complex pricing models for various AWS Generative AI services and implementing strategic approaches to optimize infrastructure, inference, and storage costs while maintaining performance and scalability.
    • Key Tools & Services: Amazon Bedrock, Amazon SageMaker, AWS Lambda, Amazon S3, Amazon DynamoDB, AWS API Gateway, Amazon OpenSearch Service, AWS Glue, Amazon CloudWatch, AWS Identity and Access Management (IAM), AWS SDKs (Boto3), Python programming language, and various generative AI frameworks and libraries.

  • Benefits / Outcomes

    • Exam Readiness & Confidence: Achieve unparalleled, comprehensive preparation for the AWS Certified Generative AI Developer Professional exam, significantly boosting your confidence and increasing the likelihood of passing on the first attempt.
    • Validate Expert Skills: Officially certify your advanced expertise in designing, developing, deploying, and maintaining sophisticated generative AI solutions across the entire lifecycle on the AWS cloud platform.
    • Accelerated Career Advancement: Elevate your professional profile, distinguish yourself in the competitive job market, and unlock new opportunities in specialized AI/ML engineering and generative AI development roles.
    • Reinforce Practical Knowledge: Solidify your practical understanding of key AWS generative AI services, architectural patterns, and best practices through exposure to realistic, scenario-based questions.
    • Identify & Address Knowledge Gaps: Efficiently pinpoint specific knowledge gaps across all exam domains for targeted study, leveraging detailed explanations to transform weaknesses into strengths.
    • Stay Technologically Current: Ensure your knowledge is fully aligned with the latest AWS generative AI advancements, features, and operational best practices, directly reflecting the November 2025 exam objectives.
    • Strategic Problem-Solving: Develop a refined, strategic, and critical-thinking approach to tackling complex, multi-service generative AI problems, invaluable for both exam success and real-world project execution.

  • PROS

    • High-Quality Practice: Offers 4 professional-level, realistic practice exams, providing extensive and authentic preparation that closely mirrors the actual certification experience.
    • Up-to-Date Content: The “November 2025 update” guarantees thorough relevance and alignment with the latest AWS services, features, and official exam objectives, ensuring current study material.
    • Targeted & Efficient: Specifically designed to focus solely on the AWS Certified Generative AI Developer Professional exam, allowing candidates to efficiently allocate their study time.
    • Diagnostic Value: Serves as an excellent diagnostic tool for objectively gauging readiness and precisely identifying areas of strength and specific knowledge gaps requiring further study.
    • Positive Feedback: A solid 3.70/5 rating from 183 students indicates a generally positive reception, suggesting that a significant number of learners found the practice exams valuable and effective.

  • CONS

    • Practice-Only Focus: This course exclusively provides practice examinations and is not structured as a comprehensive learning curriculum for acquiring new generative AI concepts or AWS services from the ground up, assuming prior foundational knowledge.
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