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Understanding functional and technical aspects of Google Professional Data Engineer Exam Designing data processing systems

The following will be discussed here:

• Tradeoffs involving latency, throughput, transactions
• Capacity planning
• Choice of infrastructure
• Batch and streaming data (e.g., Cloud Dataflow, Cloud Dataproc, Apache Beam, Apache Spark and Hadoop ecosystem, Cloud Pub/Sub, Apache Kafka)
• Schema design
• Designing data processing systems
• Mapping storage systems to business requirements
• At least once, in-order, and exactly once, etc., event processing
• Data publishing and visualization (e.g., BigQuery)
• System availability and fault tolerance
• Online (interactive) vs. batch predictions
• Selecting the appropriate storage technologies
• Data modeling
• Distributed systems
• Job automation and orchestration (e.g., Cloud Composer)
• Use of distributed systems
• Hybrid cloud and edge computing
• Architecture options (e.g., message brokers, message queues, middleware, service-oriented architecture, serverless functions)
• Designing data pipelines

Professional Data Engineer Exam Details

Like other Google exams, this exam also consists of multiple choice and multiple select questions. Consider the fact that you need to pay $200 for the registration. After that, you will access the test for 2 hours which is presented either in English or Japanese. Moreover, you can either take the exam online or have to find a test center near your place to take this test.

There is no formal prerequisite for the exam but it is recommended to have 3-4 years of experience within the data engineering field and to be responsible for the tasks related to data engineering and machine learning. So, on the final test day, you need to have exhaustive knowledge about these domains to perform your best.

• Designing data processing systems
• Building data processing systems
• Operationalizing machine learning models
• Providing solution quality

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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:

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

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.

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

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