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

Exam Topics

The syllabus of the Google Professional Data Engineer exam is divided into 4 topics, each covering specific knowledge and skills that the candidates need to develop while preparing for the test. A full outline of the exam content can be viewed on the official website. The highlights of the domains covered in the test are as follows:

Topic 1. Designing Data Processing Systems

To answer the questions related to this first topic of the certification exam, the individuals need to demonstrate their proficiency in selecting the proper storage technologies. This includes their understanding of data modeling, schema design, distributed systems, as well as tradeoffs involving throughput, latency, and transactions. Moreover, the applicants need to have the ability to map storage systems to the business needs. It also measures one’s skills in designing data pipelines, designing a data processing solution, as well as migrating data warehousing & data processing.

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

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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:

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

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:

• Create ML models using BigQuery ML, APIs, and AutoML.
• 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

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