Azure AI-102 study plan

Provision both a multi-service Azure AI services resource and a single-service resource, then contrast their endpoint, key, and billing behavior

Configure managed identity, diagnostic settings to Log Analytics, and network restrictions (selected networks or private endpoint) on the provisioned resources

Assign least-privilege RBAC roles (Cognitive Services User vs. Contributor) and verify access with a second test principal

Estimate monthly cost for a planned workload with the pricing calculator and attach a budget alert to the resource group

Automate resource deployment with a GitHub Actions workflow using a service principal or federated credential

Authenticate application code to Azure OpenAI with DefaultAzureCredential instead of account keys

Deploy a GPT model and call chat completions from an SDK client with system, user, and assistant messages

Configure content filtering severity thresholds and model parameters (temperature, max tokens, top_p) and observe their effect

Implement retrieval-augmented generation using Azure AI Search as the vector store with an embedding model

Chunk and embed a document set using two different chunking strategies and compare retrieval quality

Engineer prompts that enforce groundedness, citation, and refusal behavior when context is insufficient

Implement function/tool calling so the model invokes an application function and consumes its result

Evaluate generated answers for groundedness and relevance against a fixed test question set

Create an agent in Azure AI Agent Service with explicit instructions, model deployment, and a grounding data source

Attach at least two tool types (function calling, file search, or code interpreter) and validate tool selection

Implement connected or multi-agent handoff so a routing agent delegates to a specialist agent

Add responsible AI controls: content filtering, instruction-injection defenses, and a refusal path for out-of-scope requests

Persist conversation state across sessions by managing threads and message history

Add retry, timeout, and graceful degradation handling around model and tool calls

Run OCR over a document set with the Image Analysis read capability and map results to structured fields

Compare OCR output quality across at least three image variations (rotation, low contrast, handwriting)

Train a custom image classification model on a labeled dataset and read its precision, recall, and per-class metrics

Deploy the trained model to an endpoint and consume it from a script with confidence thresholds

Implement object or face detection and apply the responsible AI restrictions that limit face-based scenarios

Choose between prebuilt, custom, and generative vision approaches for three sample business scenarios and justify each choice

Extract sentiment, key phrases, entities, and linked entities from a text corpus with the language service

Detect and redact PII across a sample dataset and verify redaction completeness

Build a conversational language understanding project with at least 5 intents and 3 entity types, then train and evaluate it

Create a question answering knowledge base and connect it to a client application

Build an Azure AI Search index with a skillset that runs OCR, key phrase extraction, entity recognition, and at least one custom skill

Project enriched output into a knowledge store and query the projected objects

Select an indexing strategy (push vs. pull, incremental enrichment, field mappings) for an updating data source

Complete a timed 50-question practice set and remediate every missed item by domain