Google Certified Professional Data Engineer Exam (Professional-Data-Engineer日本語版) - Professional-Data-Engineer日本語

Introduction

Data engineers are responsible for finding trends in data sets and developing algorithms to help make raw data more useful to the enterprise. This IT role requires a significant set of technical skills, including a deep knowledge of SQL database design and multiple programming languages They collect, transform, and visualize data. The Data Engineer designs, builds, maintains, and troubleshoots data processing systems with a particular emphasis on the security, reliability, fault-tolerance,scalability, fidelity, and efficiency of such systems.

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How to book Google Professional Data Engineer Exams

The registration for the Google Professional Data Engineer Exam follows the steps given below.

• Step1: Visit the Google Cloud Webassessor Website
• Step3: Search for the exam name Google Professional Data Engineer
• Step4: Take the date of the exam, choose exam center and make further payment using payment method like credit/debit etc.
• Step2: Sign in or sign up to your Google Cloud Webassessor account

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

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

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

The following will be discussed here:

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

Google Professional-Data-Engineer日本語 Exam Syllabus Topics: