[100% Off] Aws Generative Ai Developer Professional: 1500 Questions
Covers Bedrock models, prompt engineering, agents, RAG workflows, tuning, deployments, and GenAI security
What you’ll learn
- Understand Bedrock models
- providers and generative task patterns.
- Design prompts with guardrails and structured
- reliable outputs.
- Build agent-style workflows that orchestrate tools and services.
- Create RAG pipelines that connect models to enterprise knowledge.
- Evaluate
- tune and compare generative behaviors across experiments.
- Plan deployments and integrations for GenAI services on AWS.
- Monitor quality
- cost and drift in generative AI workloads.
- Apply security and responsible AI principles to GenAI solutions.
Requirements
- Basic understanding of AWS and cloud-native application concepts.
- Familiarity with core ML or AI ideas is helpful but not required.
- Interest in building real generative AI features
- not only prototypes.
- Willingness to review explanations and revisit weaker domains.
- Optional access to AWS documentation or a lab environment.
Description
The AWS Generative AI Developer Professional: 1500 Questions course is designed to give you a clear, end-to-end path through Bedrock models, prompt engineering, agents, RAG workflows, tuning, deployments and GenAI security. All questions are written as realistic design and implementation scenarios, so you practice thinking like a developer who builds and ships generative AI systems on AWS.
This course contains 1,500 questions divided into six sections of 250 questions each, aligned with the lifecycle of production-grade generative AI applications.
You begin with Bedrock Foundations, Model Catalog & Generative Patterns — 250 Questions, where you clarify how Amazon Bedrock, model choices and task patterns shape your overall architecture.
The second section, Prompt Engineering, Guardrails & Structured Output Design — 250 Questions, focuses on prompt strategies and safety-aware design, helping you create stable, structured and policy-aligned outputs.
In the third section, Agents, Tools, Workflows & Orchestrated GenAI Applications — 250 Questions, you work with agent-style patterns and tool calls, learning when orchestrated flows add value and how to keep them observable.
The fourth section, Retrieval-Augmented Generation (RAG), Knowledge Sources & Context Strategies — 250 Questions, turns to RAG architectures, showing how to connect models with enterprise data, vector indexes and knowledge sources in a controlled way.
The fifth section, Evaluation, Tuning, Experiments & Performance Optimization — 250 Questions, develops your ability to evaluate, compare and tune generative behaviors, so improvements are measurable and repeatable.
Finally, the sixth section, Deployments, Monitoring, Security & Responsible GenAI Operations — 250 Questions, connects everything into secure, monitored and governed deployments, where quality, safety and cost are all actively managed.
Each practice test can be retaken as many times as you need, helping you track and enhance your progress, reinforce weaker areas and build structured confidence. Whether you are targeting advanced generative AI roles on AWS or integrating GenAI into existing products, this course gives you a clear, section-based route to professional-level generative AI development skills.
