AWS AI Practitioner study plan

Define generative AI, foundation models, large language models, and tokenization in your own words without notes

Distinguish pretraining, fine-tuning, and in-context learning by writing one concrete example of each

Explain embeddings, vectors, and semantic similarity well enough to sketch how a retrieval system matches a question to a document

Compare foundation model capabilities and limitations against traditional supervised ML for three business scenarios

Describe how transformer attention enables context handling and explain why context windows constrain prompt design

Differentiate prompting techniques (zero-shot, few-shot, chain-of-thought) and select the right one for four sample tasks

Identify sources of hallucination and non-determinism and propose one mitigation for each

Compare Amazon Bedrock, Amazon SageMaker JumpStart, and Amazon Q at a conceptual level for model access and use

Map foundation model application patterns (text generation, summarization, RAG, agents, image generation) to business use cases

Explain retrieval-augmented generation end to end, including ingestion, chunking, embedding, vector search, and augmented prompt construction

Describe how Amazon Bedrock supports model choice, knowledge bases, guardrails, and agents without managing infrastructure

Evaluate a proposed use case for feasibility, cost drivers, and latency trade-offs

Distinguish supervised, unsupervised, and reinforcement learning and match each to a realistic example

Explain the roles of training, validation, and test data splits and why leakage invalidates evaluation

Select evaluation metrics (accuracy, precision, recall, F1, RMSE) appropriate to classification and regression problems

Describe how foundation model evaluation differs from classical ML evaluation, including human review and benchmark limits

Explain the AWS shared responsibility model as it applies to AI workloads and identify which controls remain the customer's

Describe core AWS AI services (Amazon Rekognition, Comprehend, Transcribe, Polly, Textract, Translate) and match each to a task

Differentiate Amazon SageMaker capabilities for building, training, and deploying custom models at a conceptual level

Identify which AWS AI service or approach fits five given business requirements

Define the core responsible AI dimensions: fairness, explainability, privacy, safety, controllability, verifiability, and robustness

Identify bias sources across data collection, labeling, model training, and deployment, and propose a mitigation for each stage

Explain how Amazon Bedrock Guardrails filters harmful content, redacts sensitive information, and grounds responses

Draft transparency and human-oversight requirements for a customer-facing generative AI assistant

Apply least-privilege access control to AI workloads using IAM roles, policies, and separation of duties

Describe data protection practices for AI systems including encryption at rest and in transit, and handling of sensitive data in prompts

Explain logging, monitoring, and auditability requirements for generative AI applications using AWS services

Identify governance practices for model inventory, change management, and regulatory and compliance considerations

Consolidate weak areas by re-reviewing every missed item from prior quizzes and deliverables

Complete two timed full-length practice exams and record per-domain accuracy

Explain each missed practice question aloud or in writing, citing the underlying concept rather than memorizing the answer

Build a final one-page summary sheet covering the highest-weight domain concepts for last-minute review