Generative AI concepts, capabilities and limitations, and AWS generative AI infrastructure.
This domain covers 24% of the exam. It focuses on foundational generative AI concepts (tokens, chunking, embeddings, vectors, prompt engineering, transformer-based LLMs, foundation models, multi-modal models, diffusion models), potential use cases (image/video/audio generation, summarization, chatbots, code generation), the foundation model lifecycle, advantages and disadvantages of generative AI (hallucinations, nondeterminism), factors for selecting appropriate models, AWS services for building generative AI applications (Amazon Bedrock, SageMaker JumpStart, PartyRock, Amazon Q), and cost tradeoffs of AWS generative AI services.
5 minutes
5 Questions
Domain 2: Fundamentals of Generative AI is a critical section of the AWS Certified AI Practitioner (AIF-C01) exam, covering approximately 24% of the total exam content. This domain focuses on understanding the core concepts, capabilities, and limitations of generative AI technologies.
**Key Areas Covered:**
1. **Generative AI Concepts:** This includes understanding how generative AI differs from traditional AI/ML. Candidates must grasp foundation models (FMs), large language models (LLMs), and how they are pre-trained on vast datasets to generate text, images, code, and other content. Concepts like transformers, diffusion models, GANs, and VAEs are essential.
2. **Foundation Models:** Understanding the lifecycle of foundation models, including pre-training, fine-tuning, and inference. Candidates should know about model customization techniques such as prompt engineering, Retrieval-Augmented Generation (RAG), and fine-tuning with domain-specific data. AWS services like Amazon Bedrock play a central role here.
3. **Prompt Engineering:** This covers techniques for designing effective prompts, including zero-shot, few-shot, and chain-of-thought prompting. Understanding how prompt design impacts model output quality is crucial.
4. **Generative AI Applications:** Candidates must identify appropriate use cases such as text summarization, content generation, code generation, chatbots, and image creation. Understanding when generative AI is suitable versus traditional ML approaches is key.
5. **Model Limitations and Challenges:** This includes awareness of hallucinations, bias, toxicity, and non-deterministic outputs. Candidates should understand strategies to mitigate these issues, including guardrails, human oversight, and validation mechanisms.
6. **Training and Inference:** Understanding computational requirements, tokenization, embeddings, context windows, temperature settings, and parameters like top-k and top-p that influence model behavior.
This domain ensures practitioners understand the theoretical and practical foundations of generative AI, enabling them to make informed decisions when building and deploying generative AI solutions on AWS infrastructure.Domain 2: Fundamentals of Generative AI is a critical section of the AWS Certified AI Practitioner (AIF-C01) exam, covering approximately 24% of the total exam content. This domain focuses on understanding the core concepts, capabilities, and limitations of generative AI technologies.
**Key Areas …
