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Completing the exam associated with the Google Professional Data Engineer certification provides you with a great validation of your skills in designing, building, operationalizing, securing, and monitoring data processing systems. The job roles that you can take up after getting certified include a Google Cloud Data Engineer, an Operations Engineer, a Cloud Infrastructure Engineer, a DevOps Infrastructure Engineer, a Cloud Database Engineer, a Google Cloud IAM Engineer, a DataOps Engineer, a Big Data Engineer, a Google Cloud Platform Data Architect, and more. The average salary that you can expect to earn with this certificate is around $125,550 per year. However, the real remuneration will depend on a specific job title, location of an individual, and his/her working experience.

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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:

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

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

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

The following will be discussed here:

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