Passing exam with Professional-Data-Engineer latest training materials

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

Design Data Processing Systems

• 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;
• 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.
• 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 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;

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Operationalize ML Models

• Select the Relevant Training & Service Infrastructure: The consideration for this topic includes distributed versus single machine, hardware accelerators (such as TPU and GPU), and edge compute usage;
• Leverage Pre-Built Machine Learning Models as a Service: It covers one’s knowledge and skills in customizing machine learning APIs, including Auto ML text and Auto ML Vision. It also covers the conversational experiences, such as Dialogflow as well as machine learning APIs, including Speech API and Vision API;
• Deploy Machine Learning Pipelines: This objective requires your competence in ingesting relevant data, continuous evaluation, and retraining of ML models (Kuberflow, BigQuery Machine Learning, Cloud Machine Learning Engine, and Spark Machine Learning);
• Measure, Troubleshoot & Monitor Machine Learning Models: The focus of this subtopic includes the effect of dependencies on machine learning models. It will also measure the examinees’ understanding of machine learning terminologies, such as features, regression, labels, classification, models, recommendation, evaluation metrics, and unsupervised & supervised learning. Moreover, it will also assess their knowledge of common sources of error such as assumptions regarding data.

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

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Google Professional-Data-Engineer Exam Syllabus Topics: