How to study the Professional Machine Learning Engineer - Google

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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:

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

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

Preparation Process

The candidates for the Google Professional Machine Learning Engineer certification can find everything they need to efficiently prepare for the qualifying test on the official website. The most recommended resource offered by the vendor is the Machine Learning Engineer learning path. It contains both lessons and practical labs for a comprehensive understanding of the exam content. Moreover, the students can take advantage of the sample questions designed to help the potential test takers familiarize themselves with the possible exam questions. Finally, the applicants can opt for the Machine Learning Engineer Prep Webinar to join the Google experts and recently certified professionals for the tips and insights on the Machine Learning models, data processing systems, solution quality, and more.

Prerequisites

The Google Professional Machine Learning Engineer certification exam has no formal prerequisites. However, it is pretty hard to pass this test without having solid practical background. The candidates are recommended to have at least three years of industry experience, involving about one year of experience in designing and managing solutions with the help of Google Cloud. The target individuals can take advantage of Google Cloud Free Tier to use the selected products free of charge and gain the real-world expertise.

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