Are you ready to pass one of the most challenging and career-defining AI certifications on the market? The Google Professional Machine Learning Engineer (PMLE) exam is widely regarded as the toughest AI/ML certification available — and this course gives you exactly what you need to pass it on your first attempt.This course includes 5 full-length practice exams with 300 scenario-based questions meticulously crafted to mirror the actual PMLE exam experience. Every question is tied to a real exam domain, includes a detailed explanation, and is designed to expose the exact trade-offs and edge cases that trip up even experienced engineers on exam day.WHY THIS COURSE STANDS OUT:The PMLE exam is not a memorization test. It is a high-stakes, scenario-driven challenge that requires you to think like a senior ML engineer at Google. Our questions simulate exactly that. You will face multi-paragraph case studies requiring you to select the best Vertex AI architecture, choose between batch and online inference, decide when to use AutoML versus custom training, design CI/CD pipelines for ML models, evaluate bias and drift in production systems, and much more.WHAT IS COVERED:Domain 1 (13%): Architecting low-code AI solutions using BigQuery ML, AutoML, pre-built ML APIs, Model Garden, and RAG patterns with Vertex AI Agent Builder.Domain 2 (14%): Collaborating on data and models with Dataflow, TFX, BigQuery, Vertex AI Feature Store, Jupyter notebooks, and experiment tracking.Domain 3 (18%): Scaling prototypes to production ML models using Vertex AI custom training, Kubeflow Pipelines, hyperparameter tuning with Vertex AI Vizier, and distributed training on TPUs and GPUs.Domain 4 (20%): Serving and scaling ML models with online and batch inference, Vertex AI Endpoints, A/B testing, canary deployments, and hardware optimization for cost and latency.Domain 5 (22%): Autom
What you'll learn
understand low-code AI solutions using BigQuery ML and Vertex AI
collaborate on data and models with tools like Dataflow and BigQuery
scale prototypes to production ML models using Vertex AI and Kubeflow Pipelines
serve and scale ML models effectively with online and batch inference strategies
evaluate model performance and operationalize CI/CD pipelines for ML applications
Course objectives
help students pass the PMLE exam on their first attempt
simulate real exam conditions to prepare students effectively
provide detailed feedback and explanations for practice questions