Google Professional-Machine-Learning-Engineer actual dump : Google Professional 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:

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

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 Data Preparation and Processing

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

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

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