AI-103 · CHEAT SHEET

Microsoft Certified: Azure AI Apps and Agents Developer Associate

Key concepts grouped by official exam weight.

1. Plan and manage an Azure AI solution

25–30%

Choose an appropriate model for each task, including large language models, 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

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 pipelines

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

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

30–35%

Deploy and consume large language models, small models, code models, and multimodal models
Implement retrieval-augmented generation 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

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

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

10–15%

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

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 filters to classify unsafe or disallowed visual content
Detect and mitigate indirect prompt injection by using embedded text in images
Enforce visual policy rules, including watermarks, prohibited symbols, brand requirements, and inappropriate-content detection

10–15%

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 language-model-powered translation flows
Customize language model outputs for domain tasks, such as compliance summarization and domain extraction

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

10–15%

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 retrieval-augmented generation ingestion flow, including documents and optical character recognition
Connect retrieval pipelines directly to workflows and agent tools

Extract information by using multimodal pipelines that combine optical character recognition, layout analysis, and field extraction
Produce clean, grounded representations for agents and retrieval-augmented generation by using Content Understanding
Implement analyzers for structured or markdown outputs by using Content Understanding