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  • Total Questions: 412
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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

Understanding functional and technical aspects of Professional Machine Learning Engineer - Google ML Model Development

The following will be discussed in Google Professional-Machine-Learning-Engineer exam dumps:

  • Tracking metrics during training
  • Unit tests for model training and serving
  • Model explainability on Cloud AI Platform
  • Hardware accelerators
  • Overfitting
  • Transfer learning
  • Choice of framework and model
  • Productionizing
  • Retraining/redeployment evaluation
  • Model performance against baselines, simpler models, and across the time dimension
  • Build a model
  • 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)
  • Scale model training and serving
  • Model generalization
  • Distributed training

Understanding functional and technical aspects of Professional Machine Learning Engineer - Google ML Solution Architecture

The following will be discussed in Google Professional-Machine-Learning-Engineer exam dumps:

  • Feature engineering
  • Data connections
  • Building secure ML systems
  • Automation of data preparation and model training/deployment
  • Optimizing data use and storage
  • Serving
  • Selection of quotas and compute/accelerators with components
  • Logging/management
  • Exploration/analysis
  • A variety of component types - data collection; data management
  • Design reliable, scalable, highly available ML solution
  • SDLC best practices
  • Choose appropriate Google Cloud software components
  • Design architecture that complies with regulatory and security concerns
  • Privacy implications of data usage
  • Automation
  • Monitoring
  • Choose appropriate Google Cloud hardware components
  • Identifying potential regulatory issues

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Exam Details

The Google Professional Machine Learning Engineer exam is two hours long. The candidates can expect multiple-choice as well as multiple-select questions in their delivery of the certification test. The exam is currently given to the learners in the English language. To register for and schedule it, you need to pay $200 (plus applicable taxes). While registering for the test, the potential applicants will be offered to select the convenient mode of exam delivery: an online proctored session from a remote location or an in-person proctored session at the nearest testing center.

Reference: https://cloud.google.com/certification/guides/machine-learning-engineer

Google Professional-Machine-Learning-Engineer Exam Syllabus Topics:

SectionObjectives
Topic 1: Automating and orchestrating ML pipelines- CI/CD for ML systems
- Triggering and scheduling pipelines
- Vertex AI Pipelines (Kubeflow Pipelines)
Topic 2: Collaborating within and across teams to manage data and models- Version control and reproducibility (e.g., DVC, MLOps)
- Data management and governance
- Collaboration between Data Scientists, Data Engineers, and ML Engineers
Topic 3: Serving and scaling models- Hardware accelerators (GPU/TPU) in serving
- Model optimization (Quantization, Distillation)
- Online prediction (Vertex AI Prediction)
- Batch prediction
Topic 4: Architecting low-code ML solutions- Implementing BigQuery ML for basic models
- AutoML capabilities and implementation
- Leveraging pre-built ML models as a service (e.g., Vision AI, Speech-to-Text, Recommendations AI)
Topic 5: Scaling prototypes into ML models- Frameworks (TensorFlow, PyTorch, JAX, Scikit-learn)
- Hyperparameter tuning
- Training at scale (Distributed training, TPUs)
Topic 6: Monitoring ML solutions- Logging and alerting (Cloud Monitoring)
- Performance monitoring and drift detection
- Model retraining strategies

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