Course Includes:
- Price: FREE
- Enrolled: 0 students
- Language: English
- Certificate: Yes
- Difficulty: Beginner
Coupon verified 02:09 PM (updated every 10 min)
Course Folder
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
Requirements
- No prior certification is required. The exam is written for engineers who build and operate machine learning and generative AI workloads on Azure
- so the material assumes some exposure to that work.,Helpful but not required: • Having trained or deployed at least one model on any platform • Having read a pipeline or CI/CD workflow definition • Basic familiarity with Azure resource groups and the Azure CLI • Comfort reading configuration files such as Bicep
- YAML
- or JSON
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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