Build the foundations
Review validation, leakage, baselines, metrics and reproducible data splits.
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Show the full model lifecycle, including production failures.
Review validation, leakage, baselines, metrics and reproducible data splits.
Build feature and training pipelines with experiment tracking and versioned artefacts.
Deploy a model with tests, drift monitoring and a rollback plan; explain offline versus online metrics.
Write a model card and demonstrate one failure investigation, including fairness and data limitations.
Your checklist stays in this page session only. It is a self-check, not a skills assessment.
Pick one credential that matches the technology in your target vacancies. Check prerequisites and prove the skills with a project before booking.
Useful for moving from notebooks into repeatable machine learning workflows on Databricks.
Before booking: Practise Python, basic model training and Databricks Machine Learning.
Fits engineers putting models into production and maintaining reliable ML systems on AWS. Adds operational depth beyond an AI foundation credential.
Before booking: AWS targets about one year using SageMaker and other AWS ML engineering services.
Fits experienced engineers building and operating ML and AI solutions on Google Cloud.
Before booking: Build strong programming, data processing, model deployment and Google Cloud experience.
Review the official source for fees, assessment format, prerequisites and award/renewal requirements. Guidance reviewed 13 September 2026.
Deploy a tabular prediction model with a baseline, reproducible training, serving endpoint and drift simulation.
Use these original practice scenarios to structure your answers. They are not leaked exam questions or an employer’s interview script.
A strong answer covers: Investigate leakage, distribution shift, training-serving skew and whether the metric matches the business goal.
A strong answer covers: Define monitoring thresholds, validation gates and rollback criteria rather than retraining blindly.