Understanding functional and technical aspects of Professional Machine Learning Engineer - Google Data Preparation and Processing

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

• Handling outliers
• Statistical fundamentals at scale
• Data ingestion
• Feature selection
• Data exploration (EDA)
• Handling missing data
• Data validation
• Feature engineering
• Managing large samples (TFRecords)
• Batching and streaming data pipelines at scale
• Data privacy and compliance
• Build data pipelines
• Feature crosses
• Data leakage and augmentation
• Design data pipelines
• Database migration
• Class imbalance
• Streaming data (e.g. from IoT devices)
• Evaluation of data quality and feasibility
• Monitoring/changing deployed pipelines
• Ingestion of various file types (e.g. Csv, json, img, parquet or databases, Hadoop/Spark)
• Visualization
• Transformations (TensorFlow Transform)
• Encoding structured data types

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

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