Docker Certified Associate – Preparation & methodology
Preparing for the Docker Certified Associate (DCA) exam is an achievable goal with the right plan, focused practice, and high-quality mock exams.
Microsoft
Microsoft Certified: Azure AI Engineer Associate
The Microsoft Certified: Azure AI Engineer Associate (AI-102) certification validates skills in designing and implementing AI solutions on Microsoft Azure, including generative AI, computer vision, natural language processing, and knowledge mining.
Exam code
AI-102
Duration
150 min
Questions
60
Official Prerequisites
- Familiarity with Azure AI services including Cognitive Services, Language Studio, and Speech Services.
- Practical experience developing AI solutions using Python or C# and REST APIs.
- Understanding of machine learning concepts, model evaluation, and data preparation.
- Experience deploying and managing Azure resources using Azure Resource Manager and security best practices.
- Completion of Azure Fundamentals (AZ-900) or equivalent cloud experience recommended.
Targeted Professions
Azure AI Engineer AI Engineer Machine Learning Engineer Data Scientist AI Solutions ArchitectDomain blueprint
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Select appropriate Microsoft Foundry services and AI resources; plan, create, deploy and integrate Microsoft Foundry services (including model selection, deployment options, SDKs/APIs, CI/CD and container deployments); and manage, monitor, secure and cost-optimize AI resources while applying responsible AI practices (content moderation, safety, governance, keys, and authentication).
Design and build generative AI solutions using Microsoft Foundry and Azure OpenAI (provisioning, model selection and deployment, prompt flows, RAG/grounding, prompt templates, SDK integration, multimodal generation and image models) and operationalize them through monitoring, optimization, scalability, tracing, feedback collection and fine-tuning.
Create, configure, test and deploy custom agents using Microsoft Foundry Agent Service and the Microsoft Agent Framework; design agent orchestration and complex multi-agent workflows, support multi-user and autonomous behaviors, and optimize and validate agent performance.
Build and consume vision solutions: analyze images and video (feature selection, object detection, tagging, OCR, handwriting conversion, spatial analysis), train and publish custom vision models (classification and object detection), evaluate model metrics, and extract insights with Azure Video Indexer and Foundry vision tools.
Implement text and speech solutions including text analysis, entity/key-phrase extraction, sentiment and language detection, translation, speech-to-text and text-to-speech (SSML, custom speech), intent/keyword recognition, custom language models and conversational question-answering (train, test, publish, multi-turn and multi-language scenarios).
Domain 5
Implement natural language processing solutions
Implement text and speech solutions including text analysis, entity/key-phrase extraction, sentiment and language detection, translation, speech-to-text and text-to-speech (SSML, custom speech), intent/keyword recognition, custom language models and conversational question-answering (train, test, publish, multi-turn and multi-language scenarios).
Domain 6
Implement knowledge mining and information extraction solutions
Provision and configure Azure AI Search (indexes, data sources, indexers, skillsets, custom skills, semantic and vector solutions) and implement Document Intelligence and Content Understanding pipelines to extract, summarize, classify and project structured data (entities, tables, images) from documents, images, audio and video.
Study Tip
Use Microsoft Learn AI-102 modules, complete hands-on labs with Cognitive Services and Bot Framework, practice exam questions, and review real deployment scenarios.
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