[2026] Prepare with six expert-designed practice exams to master AWS Generative AI Developer Professional certification!
β 4.50/5 rating
π₯ 1,180 students
π November 2025 update
Add-On Information:
Get Instant Notification of New Courses on our Telegram channel.
Noteβ Make sure your ππππ¦π² cart has only this course you're going to enroll it now, Remove all other courses from the ππππ¦π² cart before Enrolling!
- Course Overview
- Strategic Exam Alignment: This course offers an intensive preparation pathway specifically calibrated for the 2026 AWS Certified Generative AI Developer Professional exam, ensuring all six practice tests reflect the most recent domain weightings and architectural standards.
- Professional-Level Depth: Unlike introductory AI content, these exams dive deep into complex, multi-tier AWS architectures, challenging students to solve intricate problems involving model orchestration, security, and enterprise-scale deployment.
- High-Fidelity Simulation: Each of the six exams is designed to replicate the actual testing environment, complete with scenario-based questions that test your ability to apply theoretical GenAI knowledge to real-world business constraints.
- Up-to-Date Question Bank: The curriculum is continuously updated through November 2025 and into 2026, incorporating the latest features of Amazon Bedrock, SageMaker JumpStart, and emerging agentic workflows that are now central to the certification.
- Comprehensive Rationale: Beyond simple answers, the course provides exhaustive explanations for every question, clarifying why specific AWS services are chosen over others and why certain configurations are considered best practices.
- Requirements / Prerequisites
- Intermediate AWS Knowledge: Students should possess a solid understanding of core AWS services, including IAM for identity management, VPC for networking, and Lambda for serverless compute, ideally at an Associate certification level.
- Machine Learning Foundations: A fundamental grasp of machine learning concepts, such as training vs. inference, loss functions, and the general lifecycle of a model, is necessary to navigate advanced GenAI topics.
- Python Scripting Proficiency: Familiarity with Python is essential, as the exams include questions based on the Boto3 SDK, LangChain frameworks, and the implementation of API calls for model interaction.
- Generative AI Literacy: Prior exposure to Large Language Models (LLMs), including concepts like tokenization, context windows, and temperature settings, will help learners grasp the professional-level questions more effectively.
- Development Environment Experience: Basic experience using the AWS Management Console and CLI is recommended to visualize the service configurations discussed throughout the practice tests.
- Skills Covered / Tools Used
- Amazon Bedrock Mastery: Detailed exploration of model selection (Claude, Llama, Titan, Jurassic), model invocation patterns, and the management of serverless AI infrastructure.
- Retrieval-Augmented Generation (RAG): Implementation of RAG architectures using Knowledge Bases for Amazon Bedrock and vector databases like Amazon OpenSearch Serverless or Pinecone.
- Advanced Prompt Engineering: Techniques for refining outputs through zero-shot, few-shot, and chain-of-thought prompting, as well as optimizing system prompts for specific model behaviors.
- Model Fine-Tuning and Customization: Understanding the technical requirements for instruction fine-tuning and continued pre-training using SageMaker JumpStart to adapt models to specialized domains.
- Agentic Workflows: Building autonomous agents using Agents for Amazon Bedrock to execute multi-step business processes through Lambda function integration and API schemas.
- AI Governance and Security: Implementation of AWS Bedrock Guardrails to enforce safety policies, PII masking, and the use of AWS PrivateLink for secure data transit.
- Performance Monitoring: Utilizing Amazon CloudWatch and AWS CloudTrail to track model latency, token consumption, and audit trail compliance for generative applications.
- Operational Optimization: Learning to balance cost and performance through Provisioned Throughput and efficient use of foundation model caching.
- Benefits / Outcomes
- Proven Certification Success: Build the mental stamina and technical confidence required to pass the AWS Generative AI Developer Professional exam on your first attempt.
- Gap Identification: Use detailed score reports to pinpoint specific areas of weakness in your GenAI knowledge, allowing for targeted study and efficient time management.
- Architectural Decision-Making: Enhance your ability to choose the right foundation model and deployment strategy based on cost, latency, and accuracy requirements for diverse use cases.
- Career Differentiation: Validate your skills in the most competitive sector of cloud computing, positioning yourself for high-level roles in AI engineering and cloud architecture.
- Mastery of Enterprise AI: Move beyond basic chatbots to understand how to build secure, scalable, and governed AI systems that meet stringent corporate compliance standards.
- PROS
- In-Depth Explanations: Each question provides a detailed breakdown of correct and incorrect options, serving as a powerful learning tool rather than just a testing mechanism.
- Scenario-Based Learning: Questions focus on practical, real-world AWS scenarios that prepare you for actual work challenges, not just rote memorization of service names.
- Frequently Updated: The course remains relevant with the fast-moving AWS release cycle, ensuring you aren’t studying outdated model versions or deprecated features.
- Balanced Difficulty: The questions are calibrated to be slightly harder than the actual exam, making the real certification feel more manageable by comparison.
- CONS
- Pure Practice Format: This course is strictly focused on practice examinations and does not include video lectures, hands-on labs, or step-by-step tutorials for building applications.
Learning Tracks: English,Development,Data Science
Found It Free? Share It Fast!