Azure AI Fundamentals (AI-901) study plan

Define artificial intelligence, machine learning, and deep learning in your own words and explain how the three relate

Classify ten everyday products as examples of AI or non-AI and justify each classification

Describe the difference between supervised, unsupervised, and reinforcement learning with one concrete scenario for each

Build a personal glossary of at least 20 AI terms with a one-sentence definition for each

Identify common computer vision workloads such as image classification, object detection, and optical character recognition

Explain how natural language processing tasks differ, including text classification, entity recognition, and sentiment analysis

Distinguish generative AI workloads from traditional predictive workloads and state when each is appropriate

Summarize the responsibilities in a responsible AI framework and match each principle to a realistic failure scenario

Describe how a model is trained, evaluated, and deployed across the machine learning lifecycle

Explain the purpose of training data, validation data, and test data splits

Interpret common evaluation metrics such as accuracy, precision, recall, and F1 score for a classification problem

Identify the ethical, privacy, and bias risks that appear at each stage of the lifecycle

Navigate the Microsoft Foundry portal and locate model catalog, playground, and project resources

Create a project and deploy a model from the catalog by using the portal interface

Explain the roles of a hub, a project, and a deployment in the Foundry resource hierarchy

Compare at least three available models on cost, capability, and intended use

Write effective system messages and prompts that constrain model behavior for a defined task

Apply prompt engineering techniques such as few-shot examples and explicit output formatting

Test a single prompt against three model configurations and record differences in output quality

Identify signs of hallucination in generated output and describe a mitigation for each sign

Explain how grounding with your own data improves answer relevance and reduces unsupported claims

Describe the purpose of embeddings and vector search in a retrieval-augmented generation pattern

Configure a Foundry solution that answers questions from a provided document set

Evaluate grounded responses for faithfulness to the source documents and note failure cases

Identify content safety, prompt injection, and data leakage risks in a generative AI solution

Describe how responsible AI controls such as content filters and evaluation metrics are applied in Foundry

Run an evaluation of a deployed model and interpret the reported quality and safety metrics

Explain how monitoring and logging support ongoing operation of an AI solution

Review weak areas identified in earlier deliverables and correct at least five misconceptions

Explain each Foundry implementation step in terms of the underlying AI concept it applies

Complete two timed practice sets and analyze every missed item by domain

State the key differences between AI concepts and Foundry implementation tasks in a consolidated summary