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What AI-103 covers—and what to prioritize
AI-103 is for developers building, managing, and deploying agents and AI solutions with Microsoft Foundry. Microsoft expects experience developing apps with Python and familiarity with general AI, generative AI, and Azure services. The published guide measures these skills as of April 16, 2026. Microsoft updates exams periodically, so check the guide again near your exam date.
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| Exam domain | Published weight | Study implication |
|---|---|---|
| Implement generative AI and agentic solutions | 30–35% | Make this the largest block of study time. |
| Plan and manage an Azure AI solution | 25–30% | Give planning, deployment, operations, and governance substantial time. |
| Implement computer vision solutions | 10–15% | Cover the full specialist domain rather than treating it as optional. |
| Implement text analysis solutions | 10–15% | Practise relevant language and speech workflows. |
| Implement information extraction solutions | 10–15% | Review retrieval pipelines and extracting structured information from documents. |
The weights make generative and agentic development, together with planning and management, the largest priorities. They do not justify skipping the three smaller domains: each still accounts for a published 10–15% of the exam.
Why a training-day review needs firsthand details
A useful review of a specific training day would need evidence about its agenda, instructor, delivery, exercises, and what participants actually practised. None of those session details is established here, so it would be misleading to rate the day or claim it prepared attendees for particular exam tasks. What can be assessed is how a training day’s coverage would map to Microsoft’s blueprint: compare its topics and practical work with the five domains and skill areas below.
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Check for more than prompt writing
The generative AI and agentic domain includes model and service selection, retrieval-augmented generation (RAG) and indexing, agent tools and memory, multi-agent orchestration, evaluation, and observability. A session focused only on writing prompts would leave important blueprint skills unaddressed.
Check for operational and specialist coverage
The wider blueprint includes deployment and infrastructure decisions, quotas and costs, CI/CD, monitoring, identity and network security, content safety, auditability, and human oversight. It also includes computer vision, speech, text analysis, translation, multimodal processing, and document extraction. Use the official skill list—not a course title alone—to judge whether a session’s scope matches your gaps.
A practical study plan based on the blueprint
The sequence below is a study recommendation derived from Microsoft’s domain weights and listed skills; it is not a schedule prescribed by Microsoft. Adjust it after a diagnostic assessment and an honest review of your hands-on experience.
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Diagnose your gaps and confirm the exam version
Read the official AI-103 study guide, note the skills-measured date, and take the available practice assessment if you can access it. Record weak areas by domain, then use the weights to prioritize without neglecting uncovered domains.
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Build generative AI and agent skills
Work through Microsoft’s learning paths for generative AI apps and AI agents. Practise selecting a model and Foundry service for a scenario; building a grounded retrieval app; defining agent roles, tools, and conversation tracking; connecting retrieval and custom functions; and orchestrating agents. Include safeguards, evaluation, and monitoring in your practice rather than stopping when an agent returns an answer.
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Study planning, operations, and safety
Review the blueprint’s expectations for deployment choices, infrastructure, quotas and costs, CI/CD integration, and monitoring. Practise reasoning through identity and network security, content safety, auditability, human oversight, and controls over tool access.
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Cover the specialist domains
Use the natural-language and visual-data learning paths, then work through the remaining listed skills: speech workflows, translation, vision, multimodal processing, retrieval pipelines, OCR and layout, and field extraction. Match your practice to the exact skills in the current study guide.
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Turn the outline into small hands-on exercises
Build a grounded retrieval app, a tool-using agent with an approval step, and a structured extraction or multimodal task. Review an evaluation result or trace as part of the work. These are suggested exercises mapped to the exam outline, not claims about official labs or any particular training day.
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Verify exam details before booking
Revisit Microsoft’s study guide and certification page close to your exam date. Confirm the current skills-measured date, local scheduling options, and price before booking.
Which Microsoft preparation resources to use
Microsoft recommends training and hands-on experience. Its certification page lists self-paced learning and classroom training as preparation formats, alongside an exam sandbox and a practice assessment available through AI Skills Navigator. Microsoft says sign-in is required to launch the practice assessment.
| Learning path | Published duration | Best fit in this plan |
|---|---|---|
| Develop generative AI apps in Azure | 6 hr 52 min | Generative AI app concepts and implementation. |
| Develop AI agents on Azure | 9 hr 52 min | Agent development and orchestration. |
| Develop natural language solutions in Azure | 5 hr 46 min | Text and language skills. |
| Extract insights from visual data on Azure | 7 hr 6 min | Visual-data skills. |
These are Microsoft’s published learning-path durations, not estimates of total exam preparation time. Choose self-paced study if you need to fit learning around other commitments; classroom training may suit you if you want instructor interaction. Microsoft confirms both formats, but the available information does not establish that one produces better exam outcomes. The certification page also links to Azure AI services and documentation for Vision, Video Indexer, Language, Speech, Search, Azure OpenAI, and Document Intelligence.
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Exam format and passing score
Microsoft’s AI-103 study guide states that a score of 700 or greater is required to pass. The certification page lists a 120-minute exam. Check the official pages for current regional scheduling and pricing; those details can vary or change.
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