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What is the duration, language, and format of Professional Machine Learning Engineer - Google
- Type of Questions: Multiple choice (MCQs), multiple answers
- No negative marking for wrong answers
- Duration of Exam: 120 minutes
- Language of Exam: English, Japanese, Korean
Reference: https://cloud.google.com/certification/guides/machine-learning-engineer
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The following will be discussed in Google Professional-Machine-Learning-Engineer exam dumps:
- Model generalization
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- Overfitting
- Choice of framework and model
- Distributed training
- Model explainability on Cloud AI Platform
- Unit tests for model training and serving
- Training a model as a job in different environments
- Modeling techniques given interpretability requirements
- Scalable model analysis (e.g. Cloud Storage output files, Dataflow, BigQuery, Google Data Studio)
- Hardware accelerators
- Model performance against baselines, simpler models, and across the time dimension
- Tracking metrics during training
- Transfer learning
- Retraining/redeployment evaluation
- Scale model training and serving
- Build a model
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How to book the Professional Machine Learning Engineer - Google
To apply for the Professional Machine Learning Engineer - Google, You have to follow these steps:
- Step 1: Go to the Google Official Site
- Step 2: Read the instruction carefully
- Step 3: Follow the given steps
- Step 4: Apply for the Professional Machine Learning Engineer Exam
Google Professional-Machine-Learning-Engineer Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Topic 1: Collaborating within and across teams to manage data and models | - Data management and governance - Collaboration between Data Scientists, Data Engineers, and ML Engineers - Version control and reproducibility (e.g., DVC, MLOps) |
| Topic 2: Architecting low-code ML solutions | - Leveraging pre-built ML models as a service (e.g., Vision AI, Speech-to-Text, Recommendations AI) - AutoML capabilities and implementation - Implementing BigQuery ML for basic models |
| Topic 3: Monitoring ML solutions | - Model retraining strategies - Logging and alerting (Cloud Monitoring) - Performance monitoring and drift detection |
| Topic 4: Automating and orchestrating ML pipelines | - CI/CD for ML systems - Triggering and scheduling pipelines - Vertex AI Pipelines (Kubeflow Pipelines) |
| Topic 5: Scaling prototypes into ML models | - Frameworks (TensorFlow, PyTorch, JAX, Scikit-learn) - Hyperparameter tuning - Training at scale (Distributed training, TPUs) |
| Topic 6: Serving and scaling models | - Online prediction (Vertex AI Prediction) - Hardware accelerators (GPU/TPU) in serving - Batch prediction - Model optimization (Quantization, Distillation) |








