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Career Opportunities

The certified individuals can explore a variety of job opportunities. Some of the positions that they can take up include a Software Engineer, a Cloud Architect, a Data Engineer, a Sales Engineer, a Data Scientist, a Cloud Developer, and a Kubernetes Architect, among others. The salary outlook for these job roles is an average of $128,500 per annum.

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

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

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This course will show you how to manage big data including loading, extracting, cleaning, and validating data. At the end of the training, you can easily create machine learning and statistical models as well as visualizing query results. This program is a bit lengthy but you have to practice well to get the knowledge needed on the actual exam. These are the following modules covered in the course:

• Performing Spark on Cloud Dataproc
• Building a Data Warehouse
• Bigtable Streaming Features and High-Throughput BigQuery
• Advanced BigQuery Performance and Functionality
• Introduction to Data Engineering
• Big Data Analytics with Cloud Al Platform Notebook
• Custom Model building Utilizing Cloud AutoML
• Introduction to Processing Streaming Data
• Serverless Messaging Using Cloud Sub/Pub
• Custom Model building Using SQL in BigQuery ML
• Introduction to Building Batch Data Pipelines
• Production ML Pipelines and use of Kubeflow
• Handling Data Pipelines with Cloud Composer and Cloud Data Fusion
• Serverless Data Processing with Cloud Dataflow
• Cloud Dataflow Streaming Features
• Creating a Data Lake
• Prebuilt ML Models APIs for Unsaturated Data

These modules involve everything the candidate requires for passing the Professional Data Engineer certification exam. Thus, you will not miss anything if you are taking this learning program keenly and apply the required knowledge in an appropriate way. You would end up getting a good score and achieving the Google Professional Data Engineer certification.

Operationalize ML Models

• Leverage Pre-Built Machine Learning Models as a Service: It covers one’s knowledge and skills in customizing machine learning APIs, including Auto ML text and Auto ML Vision. It also covers the conversational experiences, such as Dialogflow as well as machine learning APIs, including Speech API and Vision API;
• Measure, Troubleshoot & Monitor Machine Learning Models: The focus of this subtopic includes the effect of dependencies on machine learning models. It will also measure the examinees’ understanding of machine learning terminologies, such as features, regression, labels, classification, models, recommendation, evaluation metrics, and unsupervised & supervised learning. Moreover, it will also assess their knowledge of common sources of error such as assumptions regarding data.
• Select the Relevant Training & Service Infrastructure: The consideration for this topic includes distributed versus single machine, hardware accelerators (such as TPU and GPU), and edge compute usage;
• Deploy Machine Learning Pipelines: This objective requires your competence in ingesting relevant data, continuous evaluation, and retraining of ML models (Kuberflow, BigQuery Machine Learning, Cloud Machine Learning Engine, and Spark Machine Learning);

Google Professional-Data-Engineer日本語 Exam Syllabus Topics: