AWS Certified AI Practitioner
Validates foundational understanding of artificial intelligence and machine learning concepts, generative AI technologies, and responsible AI practices on AWS. Covers AI/ML fundamentals, foundation models, prompt engineering, Amazon Bedrock, Amazon SageMaker, and security and governance for AI solutions. Intended for individuals in any role seeking to demonstrate knowledge of AI/ML concepts and AWS AI services.
Exam domains
- Applications of Foundation Models28%
Describe design considerations for applications that use foundation models (model selection criteria - quality/cost/latency/context window/multimodal capabilities; on-demand vs provisioned throughput; agents - reasoning over tools/APIs/knowledge; RAG - retrieval augmented generation; fine-tuning vs prompt engineering vs RAG decision matrix; Amazon Bedrock Knowledge Bases for managed RAG; Bedrock Agents for tool-use orchestration). Choose effective prompt engineering techniques (zero-shot, one-shot, few-shot, chain-of-thought; system prompts vs user prompts; prompt templates; prompt injection prevention; output format constraints - JSON, XML; Bedrock Prompt Management). Describe the training and fine-tuning process for foundation models (pre-training on internet-scale corpus, continued pre-training for domain adaptation, supervised fine-tuning - SFT, reinforcement learning from human feedback - RLHF, parameter-efficient fine-tuning - PEFT/LoRA; Amazon Bedrock model customization; SageMaker JumpStart fine-tuning; data labeling with SageMaker Ground Truth; evaluation - human evaluation, automated metrics like BLEU/ROUGE/BERTScore, Bedrock model evaluation jobs). Describe methods to evaluate foundation model performance (task-specific metrics, human preference ratings, A/B testing, Bedrock evaluation - automatic + human; toxicity and bias detection; latency and cost benchmarks).
- Fundamentals of Generative AI24%
Explain the basic concepts of generative AI (foundation models - large pre-trained models adaptable to many tasks; large language models - LLMs; multimodal models - text+image+audio; diffusion models for image generation; transformer architecture; tokenization; embeddings - vector representations of meaning; emergent capabilities). Understand the capabilities and limitations of generative AI (use cases - content generation, summarization, translation, code generation, conversational AI, image/video/audio generation, semantic search via embeddings; limitations - hallucinations, knowledge cutoffs, bias, factual inaccuracy, prompt sensitivity, cost, latency, context window limits). Describe AWS infrastructure and technologies for building generative AI applications (Amazon Bedrock - fully managed foundation models from Anthropic/AI21/Cohere/Meta/Mistral/Stability AI/Amazon; Amazon Q - generative AI assistant; Amazon SageMaker JumpStart for foundation model deployment; AWS Trainium and AWS Inferentia chips for cost-optimized training/inference; AWS HealthScribe for clinical documentation).
Sources
Questions are grounded in 100 references from official and authoritative materials.