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Training Courses Recommended for the Exam Preparation

Training courses are meant to help candidates to learn about the Google exam syllabus and prepare well. It has hands-on labs and expert support that will allow you to get in-depth knowledge of each domain covered in the test. So, these are some of the best training courses offered by Google for the Professional Data Engineer certification exam.

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:

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

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

The following will be discussed here:

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