Ensure Solution Quality

• Ensure Fidelity & Reliability: The applicants should be able to carry out data preparation & quality control (such as Cloud Dataprep), verify and monitor, as well as plan, execute, and stress test data recovery (including rerunning failed jobs, fault tolerance, and retrospective re-analysis performance). Besides that, they should be able to choose between idempotent ACID and eventual consistent prerequisites;
• Ensure Efficiency & Scalability: The potential candidates will be required to demonstrate their ability to build and run test suits as well as monitor pipeline, including Stackdriver. It also focuses on their skills related to assessing, improving, and troubleshooting data process infrastructure and data representations. This area will also require that the test takers demonstrate the capacity to resize and autoscale resources;
• Design for Compliance & Security: The consideration for this topic includes identity & access management such as Cloud IAM. You should also know about data security (including key management and encryption) and privacy assurance (such as Data Loss Prevention API). This part also covers the skills needed in legal compliance, including Health Insurance Portability & Accountability Act, FedRAMP, Children’s Online Privacy Protection Act, and General Data Protection Regulation;
• Ensure Portability & Flexibility: The considerations for this domain include the design for application and data portability, including data residency prerequisites and Multiple-Cloud. It also coves data staging, discovery, and cataloging, as well as mapping to future and current business prerequisites.

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:

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

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Data Engineering on Google Cloud course

It is a 4-day course that gives hands-on experience to the candidates and allows them to build data processing systems on Google Cloud. It will also show you how to design data processing systems, analyze data and build end-to-end data pipelines and machine learning. In order to get a better understanding of the course, you need to complete the big data machine learning course or get equivalent experience. This course also aids you in developing applications using a programming language such as Python and covers the following objective:

• Designing and building data processing systems on the Google Cloud Platform
• Predicting machine models using TensorFlow and Cloud ML
• Processing batch and streaming data by using autoscaling data pipelines on Cloud Dataflow
• Enable insights from streaming data
• Influencing unstructured data using ML APIs on Cloud Dataproc