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

The following will be discussed here:

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

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

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

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:

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

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.

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