The AI-102 exam tests the ability to build AI applications using Azure AI Foundry, develop conversational agents with Azure AI Language, implement document data extraction pipelines, deploy computer vision models, and configure speech recognition and synthesis workflows. The passing score is 700 out of 1000.
The AI-102 exam tests the ability to build AI applications using Azure AI Foundry, develop conversational agents with Azure AI Language, implement document data extraction pipelines, deploy computer vision models for classification and detection, and configure speech recognition and synthesis workflows.
The exam also covers integration of multiple Azure AI services into one solution and evaluation of pipeline performance and reliability. The passing score is 700 out of 1000.
The exam is available in English, French, German, Spanish, and Italian. Abilene Academy's 3-day program covers all exam domains through hands-on labs led by active consultants, including real API-based architectures and SDK integration scenarios.
Candidates who prepare by memorizing service names consistently fail the integration scenario questions. AI-102 exam questions are case-based — they describe a business requirement and ask which combination of services, configurations, and architecture decisions solves it correctly. A candidate who knows that Azure AI Language exists but cannot explain when to use it instead of Azure OpenAI will miss the hardest tier of questions.
Well-prepared candidates have actually run code against each service family before exam day. They understand the behavioral limits of each service — latency, input constraints, error modes, and integration patterns — which is exactly what the scenario questions probe. That hands-on familiarity also makes the 3-day lab sessions productive from hour one rather than exploratory.
The AI-102 exam leads to the Microsoft Certified: Azure AI Engineer Associate credential, which validates the ability to design and implement AI solutions using Azure AI services. Certified engineers can build document intelligence pipelines, deploy conversational agents with multi-turn logic, configure computer vision models, and integrate speech and translation workflows via REST APIs and SDKs.
byRamzi AYNATI
AI-102 requires practical experience with Python or C#, familiarity with REST APIs and SDKs, and a working understanding of cloud concepts and JSON. Without these foundations, participants will struggle with service integration exercises and lab completion.
byJean MUNYARUGERERO
AI-102 training is designed for software developers building cloud-based AI applications, AI engineers integrating Azure cognitive services, and backend developers handling language, vision, or document processing in a single system. Technical consultants who need to justify AI architecture decisions to stakeholders also benefit.
byJean MUNYARUGERERO
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