AI-103: Azure AI Apps and Agents Developer Associate

This course contains the use of artificial intelligence.

AI-103: Azure AI Apps and Agents Developer Associate, is a meticulously structured Udemy course aimed at IT professionals seeking to pass the AI-103 exam. This course systematically walks you through the initial setup to advanced implementation with real-world applications.

By passing AI-103: Azure AI Engineer Associate, you're gaining proficiency in the highly recognized Microsoft AI ecosystem.

The course is always aligned with Microsoft's latest study guide and exam objectives:

Choose the appropriate Foundry services for generative AI and agents

• Choose an appropriate model for each task, including large language models (LLMs), small language models, multimodal models, and Foundry Tools

• Choose the appropriate Foundry services for generative tasks, grounding, vector search, agent workflows, or multimodal processing

• Choose an appropriate method for retrieval and indexing

• Choose appropriate memory, tool, and knowledge integration services for agent solutions

Set up AI solutions in Foundry

• Design Azure infrastructure for AI apps and agent-based solutions

• Choose appropriate deployment options

• Configure model and agent deployments

• Integrate Foundry projects with continuous integration and continuous deployment (CI/CD) pipelines

Manage, monitor, and secure AI systems

• Manage quotas, scaling, rate limits, and cost footprints for model and agent workloads

• Monitor model performance, drift, safety events, and grounding quality

• Monitor data ingestion quality, search index health, and relevance performance

• Configure security, including managed identity, private networking, keyless credentials, and role policies

Implement responsible AI across generative AI and agentic systems

• Configure safety filters, guardrails, risk detection, and content moderation

• Apply responsible AI instrumentation, including evaluators, safety evaluations, and explanation tooling

• Implement auditing through trace logging, provenance metadata, and approval workflows

• Govern agent behavior with oversight modes, constraints, and tool-access controls

Implement generative AI and agentic solutions (30–35%)

Build generative applications by using Foundry

• Deploy and consume LLMs, small models, code models, and multimodal models

• Implement retrieval-augmented generation (RAG) in an application

• Design workflows, tool-augmented flows, and multistep reasoning pipelines

• Evaluate models and apps, including detecting fabrications, relevance, quality, and safety

• Integrate generative workflows into applications by using Foundry SDKs and connectors

• Configure an application to connect to a Foundry project

Build agents by using Foundry

• Define agent roles, goals, conversation-tracking approach, and tool schemas

• Build agents that integrate retrieval, function-calling, and conversation memory

• Integrate agent tools, including APIs, knowledge stores, search, content understanding, and custom functions

• Implement orchestrated multi-agent solutions

• Build autonomous or semiautonomous workflows with safeguards and approval flow controls

• Integrate monitoring into deployed agents, evaluate agent behavior, and perform error analysis

Optimize and operationalize generative AI systems

• Tune generation behavior, such as prompt engineering and adjusting model parameters

• Implement model reflection, chain-of-thought evaluations, and self-critique loops

• Set up observability by implementing tracing, token analytics, safety signals, and latency breakdowns

• Orchestrate multiple models, flows, or hybrid LLM and rules engines

Implement computer vision solutions (10–15%)

Design and implement image- and video-generation solutions

• Implement a solution that generates images from text prompts and reference media

• Implement a solution that generates videos from text prompts and reference media

• Configure image-editing workflows, including inpainting, mask‑based edits, and prompt‑driven modifications

• Implement workflows to edit generated videos

• Select and apply appropriate generation and editing controls provided by the platform

Design and implement multimodal understanding workflows

• Build a solution that analyzes visual context by using multimodal models

• Configure apps to produce concise or detailed captions for single or multiple images

• Implement a solution that enables question‑answering grounded in visual evidence

• Configure generation of alt‑text and extended image descriptions aligned to accessibility guidelines

• Implement visual understanding by configuring Azure Content Understanding in Foundry Tools to extract visual characteristics

• Implement video analysis workflows to process and interpret video segments

• Configure single‑task and pro‑mode Content Understanding pipelines

• Implement solutions that identify objects, components, or regions within images or video

Implement responsible AI for multimodal content

• Implement filters to classify unsafe or disallowed visual content

• Detect and mitigate indirect prompt injection by using embedded text in images

• Enforce visual policy rules, such as applying watermarks, flagging prohibited symbols, upholding brand usage requirements, and detecting potentially inappropriate content

Implement text analysis solutions (10–15%)

Apply language model text analysis

• Implement solutions to extract entities, topics, summaries, and structured JSON outputs by using generative prompting and Foundry Tools

• Configure detection of sentiment, tone, safety issues, and sensitive content

• Build solutions that translate text by using Azure Translator in Foundry Tools or LLM‑powered translation flows

• Customize language model outputs for domain tasks, such as compliance summarization and domain extraction

Implement speech solutions

• Implement workflows to convert speech to text and text to speech for agentic interactions

• Integrate speech as an agent modality, including custom speech models

• Enable multimodal reasoning from audio inputs

• Translate speech into other languages by using language models and Foundry Tools

Implement information extraction solutions (10–15%)

Build retrieval and grounding pipelines

• Ingest and index content, such as documents, images, audio, and video

• Configure semantic search, hybrid search, and vector search for grounding

• Implement enrichment by using custom or built-in skills for text, images, and layout

• Configure RAG ingestion flow, including documents and using optical character recognition (OCR)

• Connect retrieval pipelines directly to workflows and agent tools

Extract content from documents

• Extract information by using multimodal pipelines that combine OCR, layout analysis, and field extraction

• Produce clean, grounded representations to use with agents and RAG by using Content Understanding

• Implement analyzers for generating structured or markdown outputs for downstream reasoning by using Content Understanding

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