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.

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Build & Operationalize Data Processing Systems

• Build & Operationalize Processing Infrastructure: The considerations for this subject area include provisioning resources, adjusting pipeline, monitoring pipeline, and testing & quality control.
• Build & Operationalize Storage Systems: This part will require the students’ skills and competence in the effective usage of managed services, including Cloud Spanner, CLoug Bigtable, BigQuery, Cloud SQL, Cloud Memorystore, Cloud Datastore, and Cloud Storage. It also covers their skills in managing the data lifecycle and storage performance and costs;
• Build & Operationalize Pipeline: This module requires that the learners demonstrate competence in data cleansing, transformation, batch & streaming, data import & acquisition, as well as integration with the new data sources;

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

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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:

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

The candidates must develop practical skills in the exam topics to succeed. These objectives are highlighted below:

Design Data Processing Systems

• Migrate Data Processing & Data Warehousing: This section includes validating migrations, migration from on-premises to Cloud, and awareness of the current state & how to migrate designs to the future state.
• Design Data Processing Solutions: This topic includes the individuals’ expertise in planning, distributed systems usage, choice of infrastructure, hybrid Cloud & edge computing, system availability & fault tolerance. You should also know about the architecture options, including message queues, message brokers, service-oriented architecture, middleware, and serverless function;
• Select the Relevant Storage Technologies: The considerations for this area include mapping storage systems to the business needs, data modeling, distributed systems, as well as tradeoffs, involving transactions, throughput, and latency;
• Design Data Pipeline: The focus for this subsection includes data visualization & publishing and batch & streaming data (Cloud Dataproc, Cloud Dataflow, Cloud Sub/Pub, Hadoop ecosystem, Apache Spark, Apache Beam, and Apache Kafka). It also focuses on online versus batch prediction and job orchestration & automation;

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