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Exam Overview

The Professional Data Engineer certification exam is a 2-hour test consisting of the multiple-choice and multiple-select questions. The students can take it in the English or Japanese languages. To register for and schedule the exam, you must pay the fee of $200. It is possible to sit for this test in an online proctored format at a remote location. You can also take it as an on-site proctored exam at a designated testing center.

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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:

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

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

This course will show you how to manage big data including loading, extracting, cleaning, and validating data. At the end of the training, you can easily create machine learning and statistical models as well as visualizing query results. This program is a bit lengthy but you have to practice well to get the knowledge needed on the actual exam. These are the following modules covered in the course:

• Bigtable Streaming Features and High-Throughput BigQuery
• Prebuilt ML Models APIs for Unsaturated Data
• Performing Spark on Cloud Dataproc
• Introduction to Data Engineering
• Big Data Analytics with Cloud Al Platform Notebook
• Custom Model building Using SQL in BigQuery ML
• Cloud Dataflow Streaming Features
• Advanced BigQuery Performance and Functionality
• Handling Data Pipelines with Cloud Composer and Cloud Data Fusion
• Custom Model building Utilizing Cloud AutoML
• Creating a Data Lake
• Serverless Data Processing with Cloud Dataflow
• Introduction to Building Batch Data Pipelines
• Serverless Messaging Using Cloud Sub/Pub
• Building a Data Warehouse
• Production ML Pipelines and use of Kubeflow
• Introduction to Processing Streaming Data

These modules involve everything the candidate requires for passing the Professional Data Engineer certification exam. Thus, you will not miss anything if you are taking this learning program keenly and apply the required knowledge in an appropriate way. You would end up getting a good score and achieving the Google Professional Data Engineer certification.