Microsoft Certified: Azure AI Engineer Associate (AI-102) Practice Test

This certification is designed for software engineers and developers who build AI-integrated applications and want to formalize their expertise on the Azure platform. Ideal candidates work in roles such as AI Engineer, Machine Learning Engineer, Cloud Developer, Solutions Architect, or Data Engineer with a focus on AI. Candidates should have hands-on development experience using Python or C# and be comfortable consuming REST APIs and SDKs across the Azure AI portfolio.

The certification is most relevant to professionals who collaborate with data scientists, data engineers, IoT specialists, and infrastructure teams to integrate AI capabilities into broader enterprise solutions. It suits those with at least one to two years of experience building cloud-based applications on Azure and some familiarity with AI/ML concepts, though it does not require deep data science or model-training expertise.

There are no mandatory prerequisites to sit for the exam, but Microsoft recommends foundational Azure knowledge equivalent to the AZ-900 (Azure Fundamentals) or AI-900 (Azure AI Fundamentals) certifications before attempting AI-102. Candidates should be proficient in Python or C# and comfortable with REST API consumption and SDK usage in at least one of those languages.

Practical experience is strongly recommended: candidates should have hands-on familiarity with Azure AI Services (including Vision, Language, Speech, and Document Intelligence), Azure AI Search, and Azure OpenAI. Understanding of cloud architecture concepts—such as resource provisioning, identity and access management, containerization, and CI/CD pipelines—will be essential for the planning and management domain. Exposure to prompt engineering, retrieval-augmented generation (RAG) patterns, and responsible AI principles is increasingly important given the exam's updated 2025 objectives.

Exam AI-102 is a proctored assessment delivered through Pearson VUE, available in both online proctored and in-person testing center formats. Candidates are given 100 minutes to complete the exam. The question count typically ranges from 40 to 60 items and may include multiple-choice, multiple-select, drag-and-drop, case studies, and interactive lab-style components. Microsoft does not publish a fixed question count, as it varies per exam form.

The passing score is 700 on a scale of 100–1000. Scoring is scaled and not a simple percentage of correct answers. The exam is available in English, Japanese, Chinese (Simplified and Traditional), Korean, German, French, Spanish, Portuguese (Brazil), and Italian. Candidates who test in a non-English language can request 30 additional minutes. If a candidate fails, they may retake the exam after 24 hours; subsequent retakes require a 14-day waiting period, and no more than five attempts are allowed within a 12-month period. The resulting certification is valid for one year and can be renewed at no cost via an online assessment on Microsoft Learn.

• 1.Plan and manage an Azure AI solution (20–25%): Selecting appropriate Microsoft Foundry services for generative AI, vision, NLP, speech, and information extraction scenarios; provisioning and deploying Azure AI resources and models; integrating services into CI/CD pipelines; container deployment; monitoring costs and performance; managing authentication and account keys; implementing responsible AI governance including content filters, blocklists, prompt shields, and harm detection.
• 2.Implement generative AI solutions (15–20%): Building solutions with Microsoft Foundry including deploying hubs, projects, and models; implementing prompt flow and retrieval-augmented generation (RAG); evaluating models and flows; integrating with the Microsoft Foundry SDK; using Azure OpenAI in Foundry Models to generate text, code, and images (including DALL-E and large multimodal models); applying prompt engineering techniques; fine-tuning models; configuring model monitoring, tracing, and feedback; and orchestrating multiple generative AI models.
• 3.Implement an agentic solution (5–10%): Understanding agent roles and use cases; configuring resources and creating agents with the Microsoft Foundry Agent Service; implementing complex agents and multi-agent orchestration workflows using Microsoft Agent Framework; enabling autonomous capabilities; and testing, optimizing, and deploying agents.
• 4.Implement computer vision solutions (10–15%): Analyzing images with Azure Vision in Foundry Tools including object detection, tagging, and text extraction (OCR and handwriting); building and consuming custom image classification and object detection models; evaluating and publishing custom vision models; and analyzing videos with Azure AI Video Indexer and Spatial Analysis for detecting people presence and movement.
• 5.Implement natural language processing solutions (15–20%): Extracting key phrases, entities, sentiment, language, and PII from text; translating text and documents with Azure Translator in Foundry Tools; implementing text-to-speech and speech-to-text with Azure Speech in Foundry Tools including SSML, custom speech, intent recognition, and speech translation; and building custom language understanding models and question answering (QnA) solutions including multi-turn conversations and multi-language support.
• 6.Implement knowledge mining and information extraction solutions (15–20%): Provisioning Azure AI Search resources, defining indexes, skillsets, and indexers; implementing custom skills; querying with full-text, semantic, and vector search; managing Knowledge Store projections; using Azure Document Intelligence in Foundry Tools with prebuilt and custom models; training and publishing custom document intelligence models; and using Azure Content Understanding in Foundry Tools to extract text, entities, tables, images, and insights from documents, images, video, and audio.

• Work through the official Microsoft Learn learning path for AI-102, which is structured by exam domain and includes hands-on sandbox exercises. Access it via the 'Two ways to prepare' section on the official certification page.
• Use the free official Practice Assessment (Assessment ID 61) on Microsoft Learn before scheduling your exam. It mirrors the wording, style, and difficulty of real questions and identifies specific knowledge gaps by domain.
• Provision a free-tier or pay-as-you-go Azure subscription and build small end-to-end projects covering each domain: deploy an Azure OpenAI resource and build a RAG pipeline, create a custom vision model, build a QnA solution, and index documents with Azure AI Search. Hands-on lab time is essential.
• Study the official AI-102 Study Guide at aka.ms/ai102-StudyGuide carefully. The December 23, 2025 version reflects current objectives. Pay special attention to the 'Implement an agentic solution' domain (5–10%), which is new and covers Microsoft Foundry Agent Service and multi-agent orchestration with Microsoft Agent Framework.
• Explore the Azure AI Foundry portal (ai.azure.com) hands-on. The exam heavily tests Microsoft Foundry workflows including hub/project deployment, prompt flow, model evaluation, and tracing—capabilities that are best learned by actually using the portal rather than reading about them.
• Review responsible AI documentation including Azure AI Content Safety, prompt shields, content filters, and blocklists. The 'Plan and manage' domain (20–25%) includes a responsible AI governance section that is frequently tested.
• Use the Exam Sandbox at https://aka.ms/examdemo to familiarize yourself with interactive question types including drag-and-drop and case study formats before exam day, so you don't lose time getting oriented during the real exam.

Earning the AI-102 certification positions professionals for high-demand roles including Azure AI Engineer, Machine Learning Engineer, Cloud AI Developer, AI Solutions Architect, and Cognitive Services Developer. According to ZipRecruiter, the average annual salary for Azure AI Engineers in the United States was approximately $158,110 as of early 2026, with senior roles at top technology companies exceeding $200,000. The certification is particularly valuable as enterprises scale generative AI adoption—engineers who can implement production-ready RAG pipelines, deploy Azure OpenAI solutions, and build agentic workflows are commanding significant salary premiums.

The AI-102 is the primary associate-level AI engineering credential in the Microsoft ecosystem, sitting above AI-900 (Azure AI Fundamentals) and complementing DP-100 (Azure Data Scientist Associate) for teams building end-to-end ML pipelines. Industry surveys indicate that 26% of certified professionals report job promotions and 35% report salary increases following certification. The exam's 2025 update to include agentic AI and Microsoft Foundry aligns the credential with current enterprise priorities, making it more relevant than its predecessors to organizations deploying Azure OpenAI at scale.