AZ MLO case studies
AZ MLO · Put your knowledge to work in context.
The Microsoft Azure Machine Learning (AZ-MLO) certification exam validates that a candidate can design, build, deploy, and govern machine learning solutions on Azure. It suits data scientists, ML engineers, and platform-oriented architects who already work with Azure Machine Learning workspaces, compute, and pipelines and want formal recognition of end-to-end solution skills. The published domain outline splits the exam evenly across five areas: designing and preparing an ML solution; exploring data and training models; deploying and operationalizing models; managing and monitoring models and experiments; and implementing responsible ML practices. Expect scenario-driven items rather than trivia, including case studies, multiple-response, drag-and-drop, and hot-area interactions that ask you to choose among plausible Azure services and configurations. The AZ MLO case studies are 5 cases and about 4,728 words.
1Feature Store Latency vs. Training Throughput
Your team trains a churn model on 40 million rows. The feature pipeline computes 120 features, 30 of which are derived from event streams that arrive with up to 6 hours of late-arriving data.
981 words, about 5 min2Autoscaling an Inference Endpoint Under Bursty Traffic
A fraud-scoring model serves a payments API. Traffic is 200 requests/second at baseline but spikes to 3,000 req/s during flash sales, lasting 2-4 minutes, several times a day.
877 words, about 4 min3Experiment Tracking When the Registry and the Notebook Disagree
A team of 8 data scientists trains models in notebooks on shared VMs. Each run writes metrics to a spreadsheet and saves the model file to a shared drive with a name like model final v3.pkl .
928 words, about 5 min4Fairness Intervention That Would Hurt the Metric
A model screens loan applications for manual review, flagging high-risk applicants. The positive class is "default." Overall AUC is 0.86.
1,030 words, about 5 min5Retraining Cadence vs. Drift You Cannot See Yet
A demand-forecasting model predicts daily order volume per SKU for 5,000 SKUs. It retrains monthly on 24 months of history.
912 words, about 5 min