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AI+ Architect Practitioner (AT-320): Exam-Focused Prep
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AI+ Architect Practitioner (AT-320): Exam-Focused Prep

Master neural networks, MLOps, generative AI & responsible design for the AI CERTs AT-320 architect exam
Created byAseem Mankotia
Last updated 11/2026
English

What you'll learn

  • Explain forward propagation, backpropagation, and loss optimization in neural networks
  • Select and tune optimizers, learning-rate schedules, and regularization strategies
  • Choose between RNN/LSTM, transformer, and embedding-based architectures for NLP tasks
  • Select CNN, transfer-learning, or vision-transformer approaches for computer vision tasks
  • Read confusion matrices, ROC-AUC, and regression metrics to drive architecture decisions
  • Design model-serving architectures using APIs, containers, GPUs, and batch/real-time patterns
  • Apply fairness, explainability, and privacy principles in responsible AI architecture design
  • Design generative AI components including RAG pipelines and vector-based retrieval
  • Evaluate new research techniques using evidence-based architecture decision frameworks
  • Synthesize a complete end-to-end AI system architecture across data, model, and serving layers

Course content

12 sections • 11 lectures • 2h 49m total length

  • Neural Network Fundamentals & the AI Architect Mental Model13:28

Requirements

  • Foundational understanding of neural networks (layers, training loop basics)
  • Basic computer science and programming knowledge (Python preferred)
  • Familiarity with at least one major cloud platform (AWS, Azure, or GCP)
  • Comfort reading and running basic Python/PyTorch code

Description

This course contains the use of artificial intelligence.

Architect-level AI roles demand more than tool knowledge — they demand the ability to reason across neural network design, evaluation, deployment, and responsible AI governance as one coherent system. This exam-focused preparation course, built by instructor Aseem Mankotia, walks you through all ten official domains of the AI CERTs AI+ Architect Practitioner (AT-320) blueprint, turning dense architecture theory into concrete design decisions you can defend in an interview or an exam room.

Each of the first ten chapters maps directly to one AT-320 exam domain — covering perceptrons and backpropagation, optimizers and regularization, transformer and CNN architecture choices, evaluation metrics, AI infrastructure and MLOps, responsible AI design, generative AI and RAG architecture, and research-based decision making. Every chapter pairs theory with a hands-on lab using real, teachable tools — PyTorch, Hugging Face transformers, FastAPI, Docker, scikit-learn, and cloud model-serving concepts from Vertex AI, SageMaker, and Azure ML — plus exam-style scenario questions, flashcards, and documented exam traps. A full capstone architecture project and a timed 90-minute, 50-question simulated exam with a required score of 35 out of 50 close out the course.

This is independent, vendor-neutral companion content designed to build the conceptual depth AT-320 assesses — it is not a replacement for official AI CERTs authorized training, and it assumes you already have foundational neural network knowledge, basic computer science background, and familiarity with at least one major cloud platform (AWS, Azure, or GCP). It is built for solution and systems architects, AI/ML engineers, IT infrastructure managers, technical leaders, and advanced students preparing for a broad, vendor-neutral AI Architect credential that complements platform-specific architecture certifications.

AI content disclosure: This course was produced with the assistance of artificial intelligence tools. Lecture narration is AI-voice generated, and lecture scripts, slides, and practice questions were drafted with AI assistance, then reviewed and curated by the instructor for technical accuracy and alignment with the official AT-320 exam guide.

Who this course is for:

  • Solution and systems architects, AI/ML engineers, IT infrastructure managers, and technical leaders who design AI systems end to end, plus students and recent graduates. Assumes foundational neural-network knowledge, basic CS, and familiarity with one major cloud (AWS, Azure, or GCP). A broad, vendor-neutral 'AI Architect' credential that complements platform-specific architecture certifications.

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