Microsoft Practice Test AI-103: Developing AI Apps and Agents on Azure

The AI-103 practice test contains 123 questions and covers the following objectives:

Plan and manage an Azure AI solution - 34 questions

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 - 40 questions

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 - 15 questions

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 - 18 questions

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 - 16 questions

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