Understanding functional and technical aspects of Google Professional Data Engineer Exam Ensuring solution quality

The following will be discussed here:

• Resizing and autoscaling resources
• Designing for data and application portability (e.g., multi-cloud, data residency requirements)
• Mapping to current and future business requirements
• Legal compliance (e.g., Health Insurance Portability and Accountability Act (HIPAA), Children's Online Privacy Protection Act (COPPA), FedRAMP, General Data Protection Regulation (GDPR))
• Identity and access management (e.g.,Cloud IAM)
• Choosing between ACID, idempotent, eventually consistent requirements
• Ensuring privacy (e.g., Data Loss Prevention API)
• Assessing, troubleshooting, and improving data representations and data processing infrastructure
• Pipeline monitoring (e.g., Stackdriver)
• Building and running test suites
• Performing data preparation and quality control (e.g., Cloud Dataprep)
• Designing for security and compliance
• Ensuring reliability and fidelity
• Data staging, cataloging, and discovery
• Ensuring flexibility and portability
• Planning, executing, and stress testing data recovery (fault tolerance, rerunning failed jobs, performing retrospective re-analysis)
• Data security (encryption, key management)
• Verification and monitoring
• Ensuring scalability and efficiency

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:

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

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.

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

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