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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.

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

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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:

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

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

SectionObjectives
Deployment and operations- Model deployment
  • 1. Deploy models using Vertex AI endpoints
    • 2. Batch and online prediction systems
      - Monitoring and maintenance
      • 1. Retraining and lifecycle management
        • 2. Monitor model drift and performance
          Data preparation and processing- Feature engineering
          • 1. Feature selection and representation techniques
            • 2. Transform and preprocess datasets
              - Data ingestion and pipelines
              • 1. Build data pipelines for training and serving
                • 2. Use BigQuery and data processing services
                  ML model development- Model training and tuning
                  • 1. Hyperparameter tuning and optimization
                    • 2. Train models using TensorFlow / Vertex AI
                      - Evaluation
                      • 1. Model validation strategies
                        • 2. Evaluate model performance metrics
                          Designing ML solutions- Framing ML problems
                          • 1. Translate business problems into ML tasks
                            • 2. Define success metrics and evaluation criteria
                              - ML architecture design
                              • 1. Design scalable ML systems on GCP
                                • 2. Select appropriate ML models and approaches
                                  ML pipeline automation and orchestration- Pipeline design
                                  • 1. Use Vertex AI Pipelines
                                    • 2. Build end-to-end ML pipelines

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