Google Cloud Professional Machine Learning EngineerStudy guide
The professional certification for building, training, serving, automating, and monitoring ML/generative AI centered on Vertex AI (Professional Machine Learning Engineer).
About Google Cloud Professional Machine Learning Engineer (GCP-PMLE)
Google Cloud Professional Machine Learning Engineer (GCP-PMLE) is a Professional / Expert-level certification from Google Cloud. This page organizes the exam scope into a 6-chapter, 12-section study guide and lets you check your understanding with exam-style practice questions. A good flow is to read the chapters below in order, then test yourself via "Practice questions."
Exam domains (approximate weighting)
- Architecting low-code AI solutions~13%
- Collaborating to manage data and models~14%
- Scaling prototypes into ML models~18%
- Serving and scaling models~20%
- Automating and orchestrating ML pipelines~22%
- Monitoring AI solutions~13%
Weights are approximate guidance for the live exam. Each domain is covered in detail in the chapters and sections below.
Official exam information: https://cloud.google.com/learn/certification/machine-learning-engineer
1Architecting low-code AI solutions
- 1.1BigQuery ML and AutoML
Understand choosing the right BigQuery ML model by business problem (linear/binary classification, regression, time-series, matrix factorization, boosted trees, autoencoders), feature engineering and prediction with BigQuery ML, and AutoML (data prep/labeling/Tabular Workflows, custom models on tabular/image/video, forecasting models, configuring/debugging trained models). Note: AutoML text was deprecated 2024-09-15 and shut down 2025-06-15 — migrate to Gemini; tabular/image/video AutoML continue.
- 1.2ML APIs, foundation models, and RAG
Understand building applications with ML APIs from Model Garden, using industry-specific APIs (Document AI API, Retail API, etc.), and implementing retrieval augmented generation (RAG) applications with Vertex AI Agent Builder.
2Collaborating within and across teams to manage data and models
- 2.1Exploring and preprocessing organization-wide data
Understand exploring organization-wide data (Cloud Storage, BigQuery, Spanner, Cloud SQL, Apache Spark, Apache Hadoop), organizing data types (tabular/text/speech/image/video), managing datasets in Agent Platform, preprocessing (Dataflow, TensorFlow Extended [TFX], BigQuery), creating/consolidating features in Feature Store on Gemini Enterprise Agent Platform, privacy of data usage (PII/PHI), and ingesting data into Agent Platform for inference.
- 2.2Notebooks and experiment tracking
Understand choosing the Jupyter backend on Google Cloud (Gemini Enterprise Agent Platform Workbench, Colab Enterprise, notebooks on Dataproc), security best practices in Gemini Enterprise Agent Platform Workbench, Spark kernels, code-repository integration, developing with frameworks (TensorFlow/PyTorch/sklearn/Spark/JAX), leveraging foundation/open-source models in Model Garden, tracking ML experiments (Experiments on Agent Platform, Kubeflow Pipelines, Vertex AI TensorBoard), and evaluating generative AI solutions.
3Scaling prototypes into ML models
- 3.1Building and training models
Understand choosing ML framework and model architecture, modeling techniques per interpretability requirements, organizing training data (tabular/text/speech/image/video), ingesting file types (CSV/JSON/images/Hadoop/DB), training with SDKs (Agent Platform custom training, Kubeflow on GKE, AutoML, Tabular Workflows), reliable distributed training, hyperparameter tuning, troubleshooting training failures, and fine-tuning foundation models.
- 3.2Choosing training hardware
Understand evaluating compute and accelerator options (CPU, GPU, TPU, edge devices) and distributed training with TPUs and GPUs (Reduction Server on Agent Platform, Horovod).
4Serving and scaling models
- 4.1Serving models
Understand batch and online inference (Agent Platform, Dataflow, BigQuery ML, Dataproc), serving with frameworks (PyTorch, XGBoost, etc.), organizing a model registry, and A/B testing model versions.
- 4.2Scaling online model serving
Understand Feature Store on Gemini Enterprise Agent Platform, Agent Platform public and private endpoints, choosing appropriate hardware (CPU/GPU/TPU/edge), scaling the serving backend by throughput (Vertex AI Prediction, containerized serving), and tuning models for training/serving in production (simplification, optimizing performance/latency/memory/throughput).
5Automating and orchestrating ML pipelines
- 5.1End-to-end ML pipelines
Understand data and model validation, consistent preprocessing between training and serving, hosting third-party pipelines (MLFlow), identifying components/parameters/triggers/compute (Cloud Build, Cloud Run), orchestration frameworks (Kubeflow Pipelines, Gemini Enterprise Agent Platform Pipelines, Cloud Composer), hybrid/multicloud strategies, and system design with TFX components or Kubeflow DSL.
- 5.2Retraining and metadata tracking
Understand determining an appropriate retraining policy, CI/CD model deployment (Cloud Build, Jenkins), tracking/comparing model artifacts and versions (Experiments on Agent Platform, Vertex ML Metadata), hooking into model/dataset versioning, and model and data lineage.
6Monitoring AI solutions
- 6.1AI risks and Responsible AI
Understand building secure AI systems by protecting against unintentional exploitation of data/models (hacking), aligning with Google's Responsible AI practices (monitoring for bias), assessing AI solution readiness (fairness, bias), and model explainability on Agent Platform (Explainable AI).
- 6.2Monitoring and troubleshooting
Understand establishing continuous evaluation metrics (Model Monitoring on Gemini Enterprise Agent Platform, Explainable AI), monitoring for training-serving skew, monitoring for feature attribution drift, monitoring model performance against baselines/simpler models/over time, and monitoring for common training and serving errors.