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The benefit of obtaining the Google Professional Data Engineer Exam Certification

A Professional Data Engineer enables data-driven decision making by collecting, transforming, and publishing data. A data engineer should be able to design, build, operationalize, secure, and monitor data processing systems with a particular emphasis on security and compliance; scalability and efficiency; reliability and fidelity; and flexibility and portability. A data engineer should also be able to leverage, deploy, and continuously train pre-existing machine learning models.

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

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The candidates must develop practical skills in the exam topics to succeed. These objectives are highlighted below:

Design Data Processing Systems

• Select the Relevant Storage Technologies: The considerations for this area include mapping storage systems to the business needs, data modeling, distributed systems, as well as tradeoffs, involving transactions, throughput, and latency;
• Design Data Pipeline: The focus for this subsection includes data visualization & publishing and batch & streaming data (Cloud Dataproc, Cloud Dataflow, Cloud Sub/Pub, Hadoop ecosystem, Apache Spark, Apache Beam, and Apache Kafka). It also focuses on online versus batch prediction and job orchestration & automation;
• Design Data Processing Solutions: This topic includes the individuals’ expertise in planning, distributed systems usage, choice of infrastructure, hybrid Cloud & edge computing, system availability & fault tolerance. You should also know about the architecture options, including message queues, message brokers, service-oriented architecture, middleware, and serverless function;
• Migrate Data Processing & Data Warehousing: This section includes validating migrations, migration from on-premises to Cloud, and awareness of the current state & how to migrate designs to the future state.

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Operationalizing Machine Learning Models

Here the candidates need to demonstrate their expertise in using pre-built Machine Learning models as a service, including Machine Learning APIs (for instance, Speech API, Vision API, etc.), customizing Machine Learning APIs (for instance, Auto ML text, AutoML Vision, etc.), conversational experiences (for instance, Dialogflow). The applicants should also have the skills in deploying the Machine Learning pipeline. This involves the ability to ingest relevant data, perform retraining of machine learning models (BigQuery ML, Cloud Machine Learning Engine, Spark ML, Kubeflow), as well as execute continuous evaluation. Additionally, the students should be able to choose the relevant training & serving infrastructure as well as know how to fulfill measuring, monitoring, and troubleshooting of Machine Learning models.

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

The following will be discussed here:

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

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