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AI-300: Machine Learning and Generative AI Solutions
New
99 students
AI-300: Machine Learning and Generative AI Solutions
6 mock exams × 100 questions • MLOps and GenAIOps judgement • Multiple-choice, multi-select, sequencing and case studies
Created byAtal Bihari Upadhyay
Last updated 10/2026
English
What you'll learn
- Design workspaces, compute, and datastores so that environments, credentials, and assets cannot leak into each other
- Define infrastructure as code with Bicep and parameter files, and keep it drift-free with deployment stacks
- Replace stored secrets with workload identity federation, managed identity, and per-environment service principals
- Automate training and deployment with `az ml` declarative jobs, pipelines, components, and GitHub Actions environments
- Track experiments properly with MLflow, then register, version, tag, and promote models with provenance
- Deploy and operate online and batch endpoints: traffic splits, readiness probes, autoscaling, rollback, and drift alerts
- Stand up GenAIOps foundations: hubs and projects, model catalog and capacity choices, prompt flows, connections, and runtime identity
- Ground generative systems with Azure AI Search: chunking, embeddings, hybrid retrieval, entitlement-aware indexes, and refresh
- Gate prompt and flow changes with offline evaluation, then describe them with versioned prompt files
- Measure quality honestly: dataset design, sample size, LLM judges, groundedness, relevance, coherence, safety, and injection testing
- Observe and diagnose: span-level tracing, prompt logging, feedback loops, and continuous evaluation on sampled traffic
- Control cost and latency without losing quality: token budgets, caching with TTL, routing, right-sizing, quantization, streaming, compression, and provisioned c
- Practise 600 original questions, including 24 case studies, with an explanation for every option
Explore related topics
Included in This Course
600 questions
Description
You can train a model. Can you ship it, gate it, watch it, and keep it affordable after the demo ends?
Prepare for Microsoft AI-300 (Microsoft Certified: Machine Learning Operations Engineer Associate) with 600 original questions across six full-length mock exams of 100 items each. Every exam is blueprint-weighted, so each one is a coherent rehearsal of the whole blueprint rather than a random topic dump.
AI-300 tests configuration, and this package honours that. You will read Bicep, `az ml` and job YAML, GitHub Actions workflows, MLflow and prompt-flow structure, endpoint traffic splits, and evaluation configuration — and decide what to change. That is deliberate: it is the one place this course departs from a purely conceptual style, because an operations engineer is judged on configuration.
WHAT YOU GET
• Six original 100-question mock exams
• Blueprint weighting on every exam: 18 / 29 / 23 / 15 / 15 across the five published domains
• Mixed item types: 76 multiple-choice, 16 multi-select, with 8 of the multiple-choice items asking you to pick the correct candidate sequence
• Four three-question case studies inside every exam
• An explanation for every option plus a case-specific overall rationale
• Local study-map skill anchors and the official study-guide reference on every item
• Offline Markdown review copies alongside instructor CSV files
• A reproducible ZIP, a validation report, and automated structural tests
EACH EXAM CONTAINS
• 18 questions on Design and implement an MLOps infrastructure
• 29 questions on Implement machine learning model lifecycle and operations
• 23 questions on Design and implement a GenAIOps infrastructure
• 15 questions on Implement generative AI quality assurance and observability
• 15 questions on Optimize generative AI systems and model performance
Who this course is for:
- Engineers preparing for AI-300 who can train a model but have not had to run one in production
- Data scientists moving from notebooks into pipelines, registries, and endpoints
- ML and platform engineers who own CI/CD, drift monitoring, and rollback
- AI engineers building retrieval-augmented and agent-based systems who need an operational release process
- Architects who must explain why a configuration is unsafe, unreproducible, or expensive
- Anyone who wants to know why the plausible answer is wrong, not just which letter is right