Google Professional-Data-Engineer Exam : Google Certified Professional Data Engineer Exam

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

The following will be discussed here:

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

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.

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

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

The following will be discussed here:

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

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