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

The following will be discussed here:

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

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

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Building & Operationalizing Data Processing Systems

Within this subject area, the test takers should show that they know how to build and operationalize storage systems. Specifically, they need to be conversant with effective use of managed services (such as Cloud Bigtable, Cloud SQL, Cloud Spanner, BigQuery, Cloud Storage, Cloud Memorystore, Cloud Datastore), storage costs & performance, and lifecycle management of data. The students should also be capable of building as well as operationalizing pipelines, including such technical tasks as data cleansing, transformation, batch & streaming, data acquisition & import, and integrating with new data sources. Apart from that, the candidates need to have sufficient competency to build and operationalize the processing infrastructure. This includes a good comprehension of provisioning resources, adjusting pipelines, monitoring pipelines, as well as testing & quality control.

Google Professional Data Engineer Practice Test Questions, Google Professional Data Engineer Exam Practice Test Questions

The Google Professional Data Engineer certification is designed to evaluate the candidates’ skills in designing data processing systems and ensuring solution quality. It is also created to measure their competence in building and operationalizing data processing systems and operationalizing ML models. The potential applicants must complete a single exam to get certified.

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