AI-102 : Microsoft Azure AI Engineer Associate Exam Voucher

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

Overview

The Microsoft Certified: Azure AI Engineer Associate (AI-102) certification validates your technical ability to design, build, deploy, and manage sophisticated artificial intelligence solutions on the Microsoft Azure platform. As artificial intelligence transforms enterprise software, organizations demand qualified engineers who can translate business requirements into scalable, secure, and production-ready AI workloads.

Earning the AI-102 credential demonstrates that you are proficient in leveraging Azure AI services, Azure OpenAI, Azure AI Search, and cognitive service models across computer vision, natural language processing, document intelligence, and generative AI. By securing an official exam voucher through KeepSub Academy, you take a cost-effective step toward earning one of the industry's most sought-after cloud credentials.

Benefits

• Industry-Recognized Credential: Prove your practical knowledge in engineering state-of-the-art AI architectures on enterprise cloud infrastructure.
• High Career Demand: AI engineering is among the fastest-growing job domains globally, with high compensation and robust job security.
• Broad AI Competency: Showcase real-world ability in both traditional cognitive services and modern Large Language Models (LLMs).
• Enterprise Value: Demonstrate your capability to implement enterprise-grade security, responsible AI governance, and performance optimization on Azure.
• Microsoft Partner Network Alignment: Help your organization meet Microsoft Solutions Partner designation criteria through certified staff.

Who should take this exam

• Software Developers building intelligent applications that integrate prebuilt or custom machine learning models.
• Cloud Engineers seeking to specialize in artificial intelligence architecture and cognitive workload automation.
• Data Engineers and Data Scientists looking to bridge the gap between model prototyping and enterprise deployment.
• AI Architects and Solutions Engineers responsible for designing scalable, ethical, and performant AI systems on Microsoft Azure.

Prerequisites

While there are no mandatory prerequisites to register for the AI-102 exam, Microsoft strongly recommends candidates possess:

• Hands-on experience developing and deploying software applications using languages such as C# or Python.
• Solid understanding of REST APIs, JSON-based data contracts, and client SDK integration.
• Fundamental knowledge of cloud computing concepts and core Azure platform services (equivalent to Azure Fundamentals AZ-900 or Azure AI Fundamentals AI-900).
• Basic familiarity with version control, software development life cycles (SDLC), and security principles like role-based access control (RBAC).

Learning outcomes

• Architect and implement secure, multi-tenant Azure AI services with proper authentication and monitoring.
• Build multimodal computer vision solutions, including image classification, object detection, and video analysis.
• Create sophisticated natural language processing pipelines using speech recognition, sentiment analysis, translation, and text analytics.
• Extract structured insights from unstructured data using Azure AI Document Intelligence and Azure AI Search.
• Harness the capabilities of Azure OpenAI Service for text generation, code synthesis, conversational interfaces, and Retrieval-Augmented Generation (RAG).
• Apply Responsible AI principles by designing solutions that incorporate content moderation, privacy, and bias mitigation.

Career opportunities

• Azure AI Engineer: Design and maintain intelligent cloud applications and automated cognitive processing pipelines.
• Generative AI Developer: Build context-aware chat applications, semantic search tools, and agentic workflows using Azure OpenAI.
• Cognitive Solutions Architect: Guide enterprise digital transformation strategies by identifying high-impact AI use cases.
• Machine Learning Integration Specialist: Package, deploy, and monitor specialized AI microservices across hybrid cloud ecosystems.

Exam syllabus

Plan and manage an Azure AI solution (15–20%)

• Select the appropriate Azure AI service: Map business requirements to Azure AI services, custom models, and prebuilt capabilities.
• Plan and configure security: Implement authentication using Microsoft Entra ID, API keys, and managed identities; secure network access using virtual networks, private endpoints, and service tags.
• Manage and monitor costs and performance: Track resource utilization, configure diagnostic logging with Azure Monitor, manage service quotas, and optimize service scaling.
• Implement responsible AI: Apply content safety checks, monitor for model drift, handle bias detection, and ensure compliance with regulatory data protection guidelines.

Implement content moderation and computer vision solutions (15–20%)

• Analyze images and video: Detect objects, extract visual features, generate tags and captions, and leverage Azure AI Vision.
• Custom vision models: Train, evaluate, and publish custom image classification and object detection models using Azure AI Custom Vision.
• Optical Character Recognition (OCR): Extract printed and handwritten text from images and documents using Read API capabilities.
• Analyze faces and emotions: Implement facial detection, identification, and emotion attributes responsibly while adhering to strict governance policies.

Implement natural language processing solutions (30–35%)

• Text analytics and language understanding: Analyze sentiment, extract key phrases, recognize linked entities, and identify languages using Azure AI Language.
• Conversational language understanding: Build, train, and test custom conversational language understanding models for bot and assistant interactions.
• Question answering: Ingest multi-source knowledge bases, configure chit-chat, and generate contextual responses.
• Speech services: Implement speech-to-text, text-to-speech, custom voice profiles, and real-time speech translation with Azure AI Speech.

Implement knowledge mining and document intelligence solutions (10–15%)

• Implement Azure AI Search: Create search indexes, build custom skillsets, configure indexers, and optimize full-text and vector search experiences.
• Vector search and semantic ranking: Integrate embeddings into search pipelines and execute hybrid semantic queries.
• Azure AI Document Intelligence: Preprocess forms, extract tables, key-value pairs, and structural data using prebuilt and custom document models.

Implement generative AI solutions (10–15%)

• Deploy Azure OpenAI models: Provision Azure OpenAI resources, deploy foundation models (GPT, DALL-E, Embeddings), and manage token usage.
• Prompt engineering and orchestration: Design system messages, apply few-shot learning, configure temperature and top-p parameters, and prevent prompt injection.
• Retrieval-Augmented Generation (RAG): Integrate Azure AI Search with Azure OpenAI to ground generative models on enterprise knowledge bases.
• Evaluate generative AI outputs: Benchmark response quality, accuracy, groundedness, and latency across custom AI applications.