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• Data ingestion
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• Data exploration (EDA)
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• Encoding structured data types
• Build data pipelines
• Visualization
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• Database migration
• Monitoring/changing deployed pipelines
• Ingestion of various file types (e.g. Csv, json, img, parquet or databases, Hadoop/Spark)
• Statistical fundamentals at scale
• Data privacy and compliance
• Handling missing data
• Design data pipelines

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The Google Professional-Machine-Learning-Engineer exam comprises multiple-choice questions, performance-based tasks, and case studies that assess the candidate's ability to design and implement machine learning solutions using Google Cloud's machine learning tools and services. Professional-Machine-Learning-Engineer exam is designed to test the candidate's knowledge of key machine learning concepts, such as supervised and unsupervised learning, deep learning, natural language processing, and computer vision. Professional-Machine-Learning-Engineer exam also evaluates the candidate's understanding of how to build scalable and reliable machine learning models that can handle large datasets.

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

Google Professional Machine Learning Engineer certification is highly respected in the industry and is recognized as a benchmark for excellence in machine learning. Achieving this certification demonstrates to employers and peers that a candidate has the skills and knowledge required to design, build, and deploy machine learning models on Google Cloud Platform. Google Professional Machine Learning Engineer certification is ideal for data scientists, machine learning engineers, software engineers, and other professionals who are looking to enhance their skills in machine learning and advance their career in this field.