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Google
Google Professional Machine Learning Engineer
The Professional Machine Learning Engineer certification by Google validates expertise in machine learning using Google Cloud.
Code examen
Professiona
Durée
240 min
Questions
70
Prérequis officiels
- Hands-on experience designing, training, and evaluating machine learning models (supervised and unsupervised) in real projects
- Proficiency in Python and ML libraries such as TensorFlow or scikit-learn
- Familiarity with Google Cloud Platform services for ML (Vertex AI, BigQuery, Cloud Storage) and basic GCP architecture
- Understanding of ML workflow concepts: feature engineering, model evaluation, deployment, monitoring, and MLOps practices
Professions ciblées
Machine Learning Engineer MLOps Engineer Data Scientist AI/ML DeveloperDomaines clés
Le moteur de simulation d'ExamBoot est synchronisé avec le plan officiel de l'examen. Nos banques de questions adaptatives priorisent l'atteinte rapide de vos objectifs..
Translating a business challenge into a well-scoped ML use case: deciding when ML is appropriate, defining success criteria and metrics, handling errors and risk, and aligning outcomes to business impact.
Designing secure, reliable, and scalable ML architectures: choosing compute (CPU/GPU/TPU), storage, and services; enforcing data constraints and privacy; orchestrating training and serving; and ensuring high availability.
Building end-to-end data pipelines: exploratory data analysis, validating and cleaning data, feature engineering (transformations, encoding, augmentation), and ensuring consistency between training and serving.
Creating and training models: selecting frameworks and algorithms, running distributed training jobs, tuning hyperparameters, evaluating against baselines, writing unit tests, and preparing models for production.
Implementing repeatable workflows: building and scheduling training and serving pipelines, versioning datasets and models, tracking metadata and lineage, and integrating CI/CD for ML.
un domaine 5
Automating and orchestrating ML pipelines
Implementing repeatable workflows: building and scheduling training and serving pipelines, versioning datasets and models, tracking metadata and lineage, and integrating CI/CD for ML.
un domaine 6
Monitoring, optimizing, and maintaining ML solutions
Ensuring model health in production: setting up monitoring and alerting for performance drift or bias, troubleshooting failures, and tuning models and infrastructure for ongoing reliability.
Conseil d'étude
Follow the official exam blueprint to align study topics, use timed blocks and practice tests, drill weak areas with hands-on labs, and review answer rationales after each timed session.
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