IT & SoftwareAdded 1 hours ago

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

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Atal Bihari Upadhyay
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AI-300: Machine Learning and Generative AI Solutions100% OFF
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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

Course 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

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