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

The following will be discussed here:

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

Exam Details

The Google Professional Data Engineer certification exam has the duration of 2 hours. The qualifying test is made up of multiple-select and multiple-choice questions. The exam is available either in Japanese or English. To register for it, you are required to go through the official webpage and pay the fee of $200 plus applicable taxes. While completing the registration process, the potential individuals can choose the preferred method of exam delivery. It can be taken in person at the nearest testing center or online from a remote location.

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

The following will be discussed here:

• Building and operationalizing data processing systems
• Effective use of managed services (Cloud Bigtable, Cloud Spanner, Cloud SQL, BigQuery, Cloud Storage, Cloud Datastore, Cloud Memorystore)
• Building and operationalizing processing infrastructure
• Building and operationalizing pipelines
• Provisioning resources
• Monitoring pipelines
• Data cleansing
• Storage costs and performance
• Building and operationalizing storage systems
• Migrating from on-premises to cloud (Data Transfer Service, Transfer Appliance, Cloud Networking)
• Batch and streaming
• Adjusting pipelines
• Testing and quality control
• Awareness of current state and how to migrate a design to a future state
• Lifecycle management of data
• Data acquisition and import
• Transformation
• Integrating with new data sources
• Validating a migration

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

Who is the Professional Data Engineer Exam Intended for?

This exam is designed for individuals who are experts in designing, building, securing, and monitoring data processing systems with a particular emphasis on compliance and security. The candidate who wants to take the Professional Data Engineer exam should have the ability to deploy, leverage, and training pre-existing machine learning models. Moreover, every applicant should have experience of more than 3 years including 1-year experience in designing and handling solutions utilizing GCP.

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