A good AI-300 study plan should not split \u201cmachine learning\u201d and \u201cgenerative AI\u201d into two unrelated halves. Microsoft\u2019s current blueprint describes one AI operations role that designs infrastructure, manages ML model lifecycle, implements GenAIOps infrastructure, evaluates and observes generative AI systems, and optimizes RAG and fine-tuned models. Study should therefore follow production dependencies: secure infrastructure first, model\/app lifecycle next, automation and deployment, then observability and optimization.<\/p>\n
Learn workspaces, datastores, compute targets, identity\/access, data\/model\/environment\/component assets, registries, and endpoint types. Build enough familiarity that you can explain where each artifact belongs and how teams share or promote assets. Do not start with exotic model optimization while the production workspace model is unclear.<\/p>\n
Then connect those objects through one deployment path: identify the workspace and identity boundary, select compute, register or reference the required assets, deploy an endpoint, and determine where logs and evaluation evidence will appear. This dependency map prevents later MLOps topics from becoming isolated product vocabulary.<\/p>\n
This is the heaviest exam area. Practice registering\/managing models, real-time versus batch endpoints, managed inference, rollout, testing, troubleshooting, monitoring, data drift, performance metrics, and retraining triggers. Build a simple lifecycle from training output to production deployment and define the evidence required to promote or roll back.<\/p>\n
For classic MLOps, devote time to endpoint behavior under failure. Test bad model packages, unhealthy deployments, quota constraints, slow scoring, or data drift conceptually or in a lab. A production model is defined partly by how safely it fails and how quickly operators can tell the difference between infrastructure and model problems.<\/p>\n
Model deployment practice should include both batch and online patterns. Ask which one matches latency, volume, cost, and workflow requirements. An online endpoint is not automatically superior; batch inference can be simpler and cheaper when immediate response is unnecessary.<\/p>\n
Keep a final one-page architecture showing Azure ML, Foundry, source control, automation, data, model\/app endpoints, monitoring, and identity. Trace one release and one incident through the diagram. If you can explain those paths clearly, the five exam domains are likely connected in your mental model.<\/p>\n
When time is limited, protect the heaviest objective first: model lifecycle and operations. Then make sure GenAIOps infrastructure is strong before spending disproportionate time on narrow optimization techniques.<\/p>\n
Add pipeline automation and CI\/CD. Study how ML pipelines, repositories, GitHub Actions, Azure CLI, and Bicep and infrastructure as code<\/a> support repeatability. Be able to identify which artifacts belong in source control, which secrets and identities must stay outside code, and how changes move through controlled environments.<\/p>\n Study identity and networking before GenAI platform details. Microsoft Foundry projects and resources still need RBAC, managed identities, private networking, and service connectivity. Draw the trust boundaries for a generative AI application and identify which identities access models, data, retrieval services, storage, and monitoring. This makes later observability and security questions more concrete.<\/p>\n Create a \u201cpromotion checklist\u201d for both ML and GenAI changes. Include code\/configuration version, environment, identity, tests, evaluation result, monitoring readiness, rollback, and ownership. The checklist trains you to think like an operations engineer rather than a notebook author. It also exposes which exam objectives you cannot yet perform confidently.<\/p>\n Include one review block for \u201cclassic ML versus GenAI\u201d differences. Both need identity, deployment, automation, monitoring, rollback, and ownership, but they differ in quality signals and failure modes. Drift in a classifier is not diagnosed exactly like a poor RAG answer or an agent tool-call loop.<\/p>\n Build GenAIOps around deployment and observability. Learn how Foundry-based apps and agents are deployed and observed. Include latency, throughput, response time, token\/resource cost, detailed logs, traces, and production debugging. The study plan should connect an operational symptom to the telemetry used to diagnose it, not just list metric names.<\/p>\n For GenAIOps, keep separate notes for infrastructure observability and quality evaluation. Logs\/traces can show tool calls, latency, errors, and token use; evaluation can show relevance, groundedness, safety, or task quality. Both are needed. A system can be operationally healthy while generating poor answers, or generate good answers while latency\/cost make it unsustainable.<\/p>\n Practice reading incident symptoms and mapping them to a domain. Endpoint errors and deployment health may point to MLOps lifecycle; token cost and traces point to GenAIOps observability; poor retrieval relevance points to optimization. This prevents random feature searching and mirrors production triage.<\/p>\n You cannot optimize what you cannot measure. Use the prompt and model evaluation<\/a> for supporting architecture. Define a test set and a small group of relevant metrics before tuning prompts, retrieval, or models. Compare versions with repeatable evidence and record trade-offs such as quality versus latency or cost.<\/p>\n GenAI optimization should be studied as experiments. Establish a baseline, change one variable, rerun evaluation, compare quality\/latency\/cost, and keep or reject the change. This structure applies to chunking, similarity threshold, embedding model, prompt, retrieval mode, and fine-tuned model changes.<\/p>\n Prioritize evidence, safe change, and repeatable operations throughout the review.<\/p>\n Cover responsible AI as an engineering gate. Review evaluation, safety, access control, data handling, and governance in the production workflow. Responsible AI controls<\/a> should attach to deployment gates, monitoring, and remediation so they operate inside the engineering lifecycle rather than in a separate policy chapter.<\/p>\n Keep responsible AI integrated with testing. Add safety or fairness criteria to evaluation where relevant and ensure the deployment process has a gate when critical criteria regress. Treat governance as part of engineering quality, not a final document prepared after technical decisions are complete.<\/p>\n Study retrieval thresholds, chunking, embeddings, hybrid search, relevance metrics, A\/B tests, fine-tuning methods, synthetic data, and tuned-model monitoring. These are optimization topics. If you study them before deployment\/evaluation\/observability, it is easy to memorize techniques without knowing how to run them safely in production.<\/p>\n Build one RAG optimization worksheet containing chunking, retrieval threshold, embedding choice, hybrid search, relevance metrics, latency, and cost. Change one variable at a time and record the expected effect. This makes the optimization domain concrete and prevents \u201ctry random prompt\/search settings\u201d from becoming your mental model.<\/p>\n Monitoring study should include alert design. A threshold without ownership or action is not useful. For drift, latency, error rate, quality, or cost, define who responds and what the next step is. This turns observability from metric memorization into an operating model.<\/p>\n Use one explicit review session for rollout and rollback patterns. Compare model endpoint deployment, prompt\/application change, retrieval-index update, and fine-tuned model release. For each, identify the known-good version, evaluation gate, production metric, and rollback mechanism. This unifies several objectives around safe change.<\/p>\n Before exam day, revisit Microsoft\u2019s study guide because service names and feature details can evolve. Keep the high-level operating model stable, but verify that the current bullets still match your notes. AI exams can move faster than traditional infrastructure exams, so source freshness is part of preparation quality.<\/p>\n Use Microsoft Learn\u2019s current study guide as the last checkpoint because the exam can evolve with the services. Refresh notes that depend on feature names or product behavior, but keep the operational model\u2014identity, deployment, monitoring, evaluation, rollback, and optimization\u2014stable.<\/p>\n Model lifecycle\/operations carries 25\u201330%, GenAIOps infrastructure 20\u201325%, MLOps infrastructure 15\u201320%, and the two GenAI QA\/optimization domains 10\u201315% each. Use weights as a starting point, then adjust based on practice performance. Do not let interesting GenAI topics crowd out the heaviest classic MLOps domain.<\/p>\n Use a baseline project throughout the study plan so skills accumulate instead of resetting with every topic. MLOps on Azure<\/a> is most useful as one operating model in which infrastructure, delivery, monitoring, evaluation, and recovery build on the same system. Start with one small ML model and one modest generative AI application or RAG flow, then apply each new skill to those systems so the dependencies remain visible.<\/p>\n Use a weighted checklist to prevent imbalance. Give roughly a quarter of practice time to model lifecycle\/operations, a little less to GenAIOps infrastructure, then MLOps infrastructure, and the remaining time to QA\/observability and optimization. Adjust based on weakness, but always keep the five official groups visible.<\/p>\nPractice evaluation before optimization<\/h2>\n
Finish RAG and fine-tuning after the operating model is stable<\/h2>\n
Allocate time according to weight and weakness<\/h2>\n
Finish with end-to-end scenarios<\/h2>\n