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• Feature engineering
• SDLC best practices
• Monitoring
• Automation
• Data connections
• Selection of quotas and compute/accelerators with components
• Identifying potential regulatory issues
• Building secure ML systems
• Optimizing data use and storage
• Privacy implications of data usage
• Serving
• Choose appropriate Google Cloud software components
• Choose appropriate Google Cloud hardware components

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

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

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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

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

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