Azure AI-900 study plan

Define generative AI and distinguish it from classical ML and discriminative workloads in your own words

Identify the core capabilities of large language models on Azure (text generation, summarization, code, image generation) and match three business scenarios to the right model family

Explain what a copilot is and list three ways Microsoft copilots differ from a plain chatbot

Label the parts of a prompt (system message, user message, few-shot examples, output constraints) and rewrite three vague prompts into well-structured ones

Sequence the steps of a retrieval-augmented generation flow (ingest, chunk, embed, index, retrieve, ground, generate) from memory

Compare prompt engineering, retrieval-augmented generation, and fine-tuning, then choose the best approach for four stated scenarios

Describe how to evaluate a generative AI response for groundedness, relevance, coherence, and harmful content

Map each of the six Microsoft responsible AI principles to a concrete generative AI failure it prevents

Classify twelve business scenarios into the correct AI workload category (computer vision, NLP, document processing, generative AI, anomaly detection)

State the six Microsoft responsible AI principles and name one process or tool that supports each

Distinguish a prebuilt AI service, a custom trained model, and a no-code studio experience such as Azure AI Foundry

Explain the roles of an Azure AI resource, endpoint, and key, and describe when a responsible AI impact assessment is required

Define machine learning and distinguish supervised, unsupervised, and reinforcement learning with one example of each

Compare regression, classification, and clustering, then select the correct technique for six stated problems

Explain the roles of features, labels, training data, validation data, and test data in a training pipeline

Describe what automated machine learning and the drag-and-drop designer in Azure Machine Learning do, and state when to use each

Calculate accuracy, precision, recall, F1 score, RMSE, and R-squared from a small confusion matrix and error table

Explain overfitting and underfitting and list three techniques that mitigate each

Describe the Azure Machine Learning workflow: workspace, compute, data asset, experiment run, model registration, and endpoint deployment

Place fairness assessment, error analysis, and interpretability at the correct stage of a responsible ML workflow

List the core computer vision capabilities in Azure (image classification, object detection, optical character recognition, facial detection) and match each to a scenario

Distinguish image classification from object detection and select the right one for four cases

Describe how optical character recognition and document intelligence extract printed text, key-value pairs, and tables

Explain the responsible AI constraints Microsoft places on face-related workloads and why they exist

List the Azure AI Language features (sentiment analysis, key phrase extraction, named entity recognition, language detection, summarization, conversational language understanding, question answering)

Distinguish conversational language understanding from question answering and choose the right one for three stated scenarios

Describe speech-to-text, text-to-speech, speech translation, and text translation scenarios and name the service that handles each

Explain how NLP features combine to support a bot or a document-processing pipeline

Re-derive the six responsible AI principles and the main generative AI design patterns from memory without notes

Complete two timed full-length practice exams under exam conditions

Review every missed item and rewrite the correct explanation in your own words

Confirm a per-domain accuracy of 75% or better in each of the five exam domains