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What is the duration, language, and format of Google Professional Data Engineer Exam

• Passing score: 80%
• Language: English (U.S.), Japanese, Spanish, and Portuguese
• Format: Multiple choices, multiple answers
• Cost: $200
• Number of Questions: 50-60
• Length of Examination: 120 minutes

Reference: https://cloud.google.com/certification/data-engineer

This course is normally taken by data scientists, data analysts, and business analysts who are in the field of Google Сloud. It is a good way to prepare for your final exam because it teaches you all the details including 7 modules that cover all the Professional Data Engineer exam objectives:

• Compute and Storage Fundamentals
• Introducing Google Cloud Platform ‘
• Additional Resources
• Machine Learning
• Data Processing Architectures
• Data Analytics on the Cloud
• Scaling Data Analytics

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Google Cloud Big Data & Machine Learning Fundamentals course

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• Utilize Cloud SQL & Dataproc to migrate existing MySQL, Pig, Spark, or Hive workloads to Google Cloud
• Hire BigQuery and Cloud SQL for interactive data analysis
• Create ML models using BigQuery ML, APIs, and AutoML.
• Recognize the purpose of the key Big data and Machine Learning products in Google Cloud

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• Deploying an ML pipeline
• Conversational experiences (e.g., Dialogflow)
• Use of edge compute
• Distributed vs. single machine
• ML APIs (e.g., Vision API, Speech API)
• Measuring, monitoring, and troubleshooting machine learning models
• Continuous evaluation
• Ingesting appropriate data
• Leveraging pre-built ML models as a service
• Hardware accelerators (e.g., GPU, TPU)
• Operationalizing machine learning models
• Choosing the appropriate training and serving infrastructure
• Impact of dependencies of machine learning models
• Machine learning terminology (e.g., features, labels, models, regression, classification, recommendation, supervised and unsupervised learning, evaluation metrics)
• Customizing ML APIs (e.g., AutoML Vision, Auto ML text)
• Retraining of machine learning models (Cloud Machine Learning Engine, BigQuery ML, Kubeflow, Spark ML)
• Common sources of error (e.g., assumptions about data)

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