Microsoft Azure AI App and Agent Developer AssociateStudy guide

Plan how to build AI apps/agents on Microsoft Foundry from a developer’s view—organizing projects and resources, selecting a fitting model (capability, modality, context length, cost, regional availability), deployment options, and estimating quota (TPM) and cost.

Understand production security for AI solutions from a developer’s view—managed identity / keyless auth, role-based access control (RBAC), private networking, monitoring and logging, and Responsible AI governance.

Understand how to implement a generative AI app by calling a deployed model with the Foundry SDK—chat completion, system message, generation parameters, streaming, and structured output (function calling)—and how to wire in safety filters (guardrails).

Understand implementing RAG for answers grounded in your data—semantic search via embeddings (vectors), Azure AI Search indexes, the retrieve → augment → generate flow, citing sources, and evaluating quality—from a developer’s view.

Understand implementing agents with Foundry Agent Service from a developer’s view—composing model, instructions, knowledge (RAG), and tools (function calling); multistep reasoning; and managing threads/runs.

Understand multi-agent design—splitting complex work across specialized agents (role division, orchestrator, handoffs between agents)—and production safety (least privilege, human-in-the-loop, monitoring, evaluation).

In AI-103, computer vision centers on generation more than recognition. Understand—from a developer’s view—deploying image- and video-generation models in Microsoft Foundry, generating images and videos from text prompts and reference media, and editing them via mask-based inpainting and prompt-driven changes.

Understand using multimodal models to comprehend visual context (captions, visual question answering, accessibility alt-text), extracting visual characteristics with Azure Content Understanding, and Responsible AI specific to multimodal content (filters for unsafe visual content, indirect prompt injection via text embedded in images, and visual policy such as watermarks).

Understand implementing text information extraction by calling Azure AI Language’s prebuilt features—sentiment analysis, key phrase extraction, entity recognition (NER), language detection, and PII detection—from a developer’s view.

Understand text translation (Azure AI Translator), custom options when prebuilt is insufficient (conversational language understanding CLU, custom classification, custom NER), and when to use flexible generative-prompt analysis.

Covers "speech," part of the official text-analysis domain. Understand—from a developer’s view—speech-to-text/text-to-speech (STT/TTS) with Azure AI Speech, speech as an agent modality with custom speech, multimodal reasoning from audio, and speech translation.

Understand implementing Azure Content Understanding to extract structured fields/insights from documents, forms, images, audio, and video—defining analyzers and schemas (extraction fields), prebuilt vs custom, and the build flow—from a developer’s view.

Understand extracting entities, topics, and summaries flexibly via prompts to a generative model—receiving structured output, grounding, evaluation, and when to use this vs prebuilt/custom.