AI-103 Certification Bootcamp
Duration
40 hours of live instructor-led training
Level
Beginner Level
Design and Tailor this course
As per your team needs
Overview
This 40-hour instructor-led program prepares participants to clear the AI-103: Developing AI Apps and Agents on Azure exam on the first attempt while building genuinely job-ready skills. The course mirrors the exam’s five official skill domains and follows a 70% hands-on / 30% concept ratio: every module pairs live instruction with guided labs in Microsoft Foundry, so participants finish having actually deployed models, built RAG pipelines, orchestrated multi-agent workflows, and implemented Responsible AI guardrails – not just read about them.
The program concludes with two full-length proctored mock exams mapped to the real AI-103 format (case studies, multi-select, drag-and-drop), individual score analysis, and a personalized revision plan. Participants receive courseware, lab guides, access to practice tests, and a course completion certificate.
CERTIFICATION EXAM COUPONS INCLUDED
This training comes bundled with the official Microsoft certification exam coupons for corporate batches and university programs. This allows the participants to get world-class training and visibility for their AI-103 exam within a single engagement.
Audience
- Software developers moving into AI application and agent development
- Cloud, data, and DevOps engineers extending their Azure skills into the generative AI layer
- AI-102 certified professionals transitioning to the new Foundry-centric credential
- Solution architects and technical consultants designing copilot and agent solutions for clients
- Corporate engineering teams standardizing on Microsoft Foundry via private batches
- University students (final year) and graduates targeting AI engineering roles
Prerequisites
- Basic Python programming – ability to read and modify scripts (functions, packages, REST calls). No advanced expertise required.
- Familiarity with cloud concepts – understanding of what resources, subscriptions, and API are. Prior Azure experience is helpful but not mandatory.
- An Azure account – a free-tier account is sufficient; lab environment setup is covered in Module 0.
- Recommended (not required): AI-900 / AI-901 fundamental knowledge, or equivalent exposure to AI/ML terminology.
Curriculum
- Program walkthrough, exam registration process, and how the certification coupon redemption works
- Azure account setup, subscription hygiene, and cost-control guardrails for labs
- Tooling: VS Code, Python environment, Azure CLI, Foundry portal access
- The Azure AI landscape: from individual cognitive services to the unified Foundry platform
- Foundry Projects: organizing deployments, agents, tools, evaluations, and connected resources
- Foundry Tools overview: retrieval, file search, code execution, vision, speech, translation
- AI-103 exam blueprint: domains, question formats, scoring, and how this course maps to them
- Hands-on lab: Create your first Foundry Project and explore the model catalog.
- Solution architecture and design trade-offs: service tiers, deployment models, regions
- Cost estimation and capacity planning for model deployments
- Security: managed identity, keyless credentials, private networking, role policies
- Governance foundations: auditing, trace logging, provenance metadata, approval workflows
- Hands-on lab: Configure a secured Foundry environment with managed identity and role-based access.
- Deploying and consuming LLMs, small models, code models, and multimodal models
- Prompt engineering patterns: system prompts, few-shot, structured outputs
- Consuming models via Foundry SDKs (azure-ai-projects) and REST
- Customizing model outputs for domain tasks: summarization, compliance, extraction
- Hands-on lab: Deploy a GPT-class model and a small model; benchmark cost, latency, and quality on the same task.
- RAG architecture: ingestion, chunking, embeddings, vector retrieval with Azure AI Search
- Grounding responses in enterprise data; handling empty or low-confidence retrieval
- Content Understanding: producing clean, grounded representations for agents and RAG
- Common RAG failure modes and how the exam tests them
- Hands-on lab: Build the capstone’s knowledge base: ingest documents, index them, and ground the copilot’s answers.
- Foundry Agent Service: agent anatomy – instructions, tools, memory, threads
- Tool-calling and function integration: connecting agents to external APIs and apps
- Multi-agent systems: roles, coordination patterns, and workflow design
- Multi-step reasoning pipelines and tool-augmented flows; governing tool-access constraints
- Hands-on lab: Build a tool-calling agent, then extend it into a two-agent researcher/writer workflow with tracing enabled.
- Image analysis, object detection, and OCR with Foundry vision capabilities
- Custom vision models: when and how to train them
- Vision as an agent capability: multimodal inputs in agentic workflows
- Hands-on lab: Add image understanding to the capstone copilot (analyze product photos submitted by users).
- NLP essentials: sentiment, entity recognition, key-phrase extraction, multilingual handling
- Translation with Azure Translator and LLM-powered translation flows
- Speech-to-text and text-to-speech workflows; custom speech models
- Integrating speech as an agent modality for voice-driven interactions
- Hands-on lab: Give the capstone copilot a voice: speech input, spoken responses, and live translation.
- Document Intelligence: prebuilt and custom extraction models
- Multimodal pipelines: OCR + layout analysis + field extraction
- Content Understanding analyzers: structured and Markdown outputs for downstream reasoning
- Feeding extracted data into RAG and agent workflows
- Hands-on lab: Process scanned invoices end-to-end and pipe the structured output into the capstone’s knowledge base.
- Safety filters, guardrails, risk detection, and content moderation
- Evaluators: detecting fabrications, measuring relevance, quality, and safety
- Responsible AI instrumentation: safety evaluations and explanation tooling
- Agent oversight: modes, constraints, and production monitoring
- Hands-on lab: Run a full evaluation suite against the capstone copilot and remediate the findings.
- Full-length mock exam #1 (timed, real format) with instructor-led answer review
- Question-type strategy: case studies, multi-select, drag-and-drop, time management
- Individual score analysis, weak-domain drilling plan, and the 80%+ green-light rule
- Full-length mock exam #2 (self-paced) + final revision checklist and exam-day logistics
Duration
40 hours of live instructor-led training
Level
Beginner Level
Design and Tailor this course
As per your team needs
