AWS AI Practitioner Course Notes

AI and machine learning questions usually start by identifying the learning pattern. Supervised learning trains from labeled examples, unsupervised learning finds structure in unlabeled data, and reinforcement learning improves decisions through rewards from an environment. On the exam, a fraud label, churn label, or known target value points to supervised learning, while clustering similar records without labels points to unsupervised learning.

A foundation model is a large pretrained model that can perform broad language, image, or multimodal tasks through prompting. Amazon Bedrock provides managed access to foundation models without requiring the customer to provision model infrastructure. The key exam distinction is whether the scenario needs to call an existing model, customize behavior with prompts and context, or train a separate custom model elsewhere.

Amazon Bedrock Knowledge Bases provide a managed RAG path for foundation model applications. A knowledge base connects source data, embedding generation, vector storage, retrieval, and model generation so an application can answer from controlled content. The exam often tests this against direct model invocation, where the model only sees the prompt and has no managed retrieval layer.

AWS AI service selection depends on how much control the scenario requires. Amazon Bedrock is the managed path for building generative AI applications with foundation models, while Amazon SageMaker is the broader ML platform for custom model development, training, deployment, monitoring, and MLOps. A question that asks for model training code, custom algorithms, or full ML lifecycle control usually points away from Bedrock and toward SageMaker.

Operating an AI solution starts with evaluation. Amazon Bedrock model evaluation helps compare model outputs against quality criteria, while human evaluation jobs bring reviewers into the process when judgment, safety, tone, or task accuracy cannot be trusted to automation alone. The exam may separate automatic evaluation from human evaluation, so watch for language about reviewers, work teams, or manual scoring.

Responsible AI on AWS is tested through concrete controls, not slogans. Amazon Bedrock Guardrails can filter harmful content, block denied topics, and apply sensitive information filters so generative AI applications have runtime safety controls. These controls sit around model use; they do not replace model evaluation, identity permissions, or human review.