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How to book Google Professional Data Engineer Exams

The registration for the Google Professional Data Engineer Exam follows the steps given below.

• Step3: Search for the exam name Google Professional Data Engineer
• Step4: Take the date of the exam, choose exam center and make further payment using payment method like credit/debit etc.
• Step1: Visit the Google Cloud Webassessor Website
• Step2: Sign in or sign up to your Google Cloud Webassessor account

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

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

The following will be discussed here:

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

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

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.

Who is the Professional Data Engineer Exam Intended for?

This exam is designed for individuals who are experts in designing, building, securing, and monitoring data processing systems with a particular emphasis on compliance and security. The candidate who wants to take the Professional Data Engineer exam should have the ability to deploy, leverage, and training pre-existing machine learning models. Moreover, every applicant should have experience of more than 3 years including 1-year experience in designing and handling solutions utilizing GCP.

Google Professional-Data-Engineer Exam Overview:

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