Google : Professional-Data-Engineer Questions & Answers

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Google Cloud Big Data & Machine Learning Fundamentals course

This course is a gateway to introduce you to Google Cloud's big data and different machine learning functions. However, to successfully pass this training, you have to attain one year of experience in SQL, extract transform, data modeling, machine learning, programming in Python, and load activities. So, the objectives of the course are the following:

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

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Understanding functional and technical aspects of Google Professional Data Engineer Exam Operationalizing machine learning models

The following will be discussed here:

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

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

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