Microsoft AI - 102 Crash Course | Azure AI Engineer Associate Exam Walkthrough (2026) скачать в хорошем качестве

Microsoft AI - 102 Crash Course | Azure AI Engineer Associate Exam Walkthrough (2026)

🌐 Start Studying for Free Today: 📘 Study Guide & Course Breakdown: https://aiexamsupport.com/AI102 🧩 Free Practice Questions (Objective Style): https://questions.aiexamsupport.com/p... 📬 Want a Guaranteed Pass? Connect with our premium tutors today: https://aiexamsupport.com/contact This is the AI 102 crash course for anyone who needs to pass fast and actually understand the material, not just memorize flashcards. If you're grinding through Azure certs to land an AI engineer role or level up from a general cloud position into something more specialized, this is the walkthrough I wish I had when I started. AI 102 sits at the intersection of traditional AI and generative AI, and the exam tests both. This session covers everything from foundational AI concepts to transformer architecture, large language models, embeddings, neural networks, GPU acceleration, and Microsoft Responsible AI principles. But more importantly, it explains how all of these connect, because the real exam doesn't test concepts in isolation. It tests them together in scenario based questions where you need to understand the architecture, not just the vocabulary. Azure AI Engineer is one of the fastest growing specializations in cloud right now. It's showing up in six figure job postings at FAANG, Fortune 500 enterprises, and Microsoft partner consultancies across the country. Whether you're at a health tech company in Seattle building AI solutions on Azure or doing cloud engineering at a financial services firm in Charlotte, this cert is a clear signal that you can design and implement AI workloads in production, not just talk about them. Here's what we cover: 🔹 Artificial Intelligence vs Generative AI and how they differ in real systems 🔹 Foundational models and fine tuning 🔹 Large Language Models and the black box effect 🔹 Transformer architecture: encoder, decoder, and attention mechanisms 🔹 Parallel processing and positional encoding 🔹 Tokenization, vocabularies, and context windows 🔹 Vector embeddings and semantic similarity 🔹 Self attention, cross attention, and multi head attention 🔹 Supervised, unsupervised, and reinforcement learning 🔹 Classification, regression, clustering, and association 🔹 Neural networks, layers, weights, loss functions, and back propagation 🔹 GPUs, parallel processing, and CUDA 🔹 Microsoft Responsible AI principles 🔹 Human AI interaction guidelines 🔹 Governance tools: Responsible AI Standard and transparency reports The exam frames questions around real world architecture decisions, so this video is structured the same way. By the end, you won't just know what a transformer is. You'll understand why it matters when the exam asks you to choose between model architectures for a specific use case. That's the difference between passing on the first try and retaking. ⚠️ Non Affiliation Disclaimer: This video is created for educational and exam preparation purposes only. We are not affiliated with Microsoft, Azure, or any official certification provider. All explanations are independent and based on publicly available learning concepts. #AI102 #AzureAIEngineer #MicrosoftAzure #GenerativeAI #LargeLanguageModels #Transformers #AiExamSupport #CloudCertification #AzureCerts #CloudCareer #AI102USA #MicrosoftAzureExamUSA #AzureAiEngineerExamUSA #USAAICertified