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

The following will be discussed here:

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

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

• 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.
• 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;
• 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;
• 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;

Requirements

The certification does not have any official prerequisites. However, it is advised to have at least three years of industry experience with one or more years of expertise in designing and managing different solutions with the use of Google Cloud Platform. It is also required to review the topics of the qualifying exam before sitting for it.

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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