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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:

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

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

The benefit of obtaining the Professional Machine Learning Engineer - Google Certification

• 87% of Google Cloud certified individuals are more confident about their cloud skills
• Professional Cloud Architect was the highest paying certification of 2020 and 2019
• More than 1 in 4 of Google Cloud certified individuals took on more responsibility or leadership roles at work

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