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Google Professional Data Engineer: Google Certified Professional Data Engineer Exam

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How to Prepare For Google Professional Data Engineer Exam

Preparation Guide for Google Professional Data Engineer Exam

Introduction to Google Professional Data Engineer Exam

Google has established a path for IT professionals endorse as a Data Engineer on the GCP platform. This accreditation program gives Google cloud professionals a way to endorse their skills. The evaluation relies on a meticulous exam using industry standard methodology to conclude whether or not a aspirant meets Google’s proficiency standards.

The Professional Data Engineer exam assesses your ability to:

  • Design data processing systems
  • Build and operationalize data processing systems
  • Operationalize machine learning models
  • Ensure solution quality

Google Professional Data Engineer Exam certification is evidence of your skills, expertise in those areas in which you like to work. If candidate wants to work on Google Professional Data Engineer and prove his knowledge, Certification offered by Google. This Google Professional Data Engineer Certification helps a candidate to validates his skills in Big Data and Data engineering Technology.

Introduction

Data engineers are responsible for finding trends in data sets and developing algorithms to help make raw data more useful to the enterprise. This IT role requires a significant set of technical skills, including a deep knowledge of SQL database design and multiple programming languages They collect, transform, and visualize data. The Data Engineer designs, builds, maintains, and troubleshoots data processing systems with a particular emphasis on the security, reliability, fault-tolerance,scalability, fidelity, and efficiency of such systems.

Understanding functional and technical aspects of Google Professional Data Engineer Exam Designing data processing systems

The following will be discussed here:

  • Designing data processing systems
  • Selecting the appropriate storage technologies
  • Mapping storage systems to business requirements
  • Data modeling
  • Tradeoffs involving latency, throughput, transactions
  • Distributed systems
  • Schema design
  • Designing data pipelines
  • Data publishing and visualization (e.g., BigQuery)
  • Batch and streaming data (e.g., Cloud Dataflow, Cloud Dataproc, Apache Beam, Apache Spark and Hadoop ecosystem, Cloud Pub/Sub, Apache Kafka)
  • Online (interactive) vs. batch predictions
  • Job automation and orchestration (e.g., Cloud Composer)
  • Choice of infrastructure
  • System availability and fault tolerance
  • Use of distributed systems
  • Capacity planning
  • Hybrid cloud and edge computing
  • Architecture options (e.g., message brokers, message queues, middleware, service-oriented architecture, serverless functions)
  • At least once, in-order, and exactly once, etc., event processing

Understanding functional and technical aspects of Google Professional Data Engineer Exam Building and operationalizing data processing systems

The following will be discussed here:

  • Awareness of current state and how to migrate a design to a future state
  • Migrating from on-premises to cloud (Data Transfer Service, Transfer Appliance, Cloud Networking)
  • Validating a migration
  • Building and operationalizing data processing systems
  • Building and operationalizing storage systems
  • Effective use of managed services (Cloud Bigtable, Cloud Spanner, Cloud SQL, BigQuery, Cloud Storage, Cloud Datastore, Cloud Memorystore)
  • Storage costs and performance
  • Lifecycle management of data
  • Building and operationalizing pipelines
  • Data cleansing
  • Batch and streaming
  • Transformation
  • Data acquisition and import
  • Integrating with new data sources
  • Building and operationalizing processing infrastructure
  • Provisioning resources
  • Monitoring pipelines
  • Adjusting pipelines
  • Testing and quality control

Understanding functional and technical aspects of Google Professional Data Engineer Exam Operationalizing machine learning models

The following will be discussed here:

  • Operationalizing machine learning models
  • Leveraging pre-built ML models as a service
  • ML APIs (e.g., Vision API, Speech API)
  • Customizing ML APIs (e.g., AutoML Vision, Auto ML text)
  • Conversational experiences (e.g., Dialogflow)
  • Deploying an ML pipeline
  • Ingesting appropriate data
  • Retraining of machine learning models (Cloud Machine Learning Engine, BigQuery ML, Kubeflow, Spark ML)
  • Continuous evaluation
  • Choosing the appropriate training and serving infrastructure
  • Distributed vs. single machine
  • Use of edge compute
  • Hardware accelerators (e.g., GPU, TPU)
  • Measuring, monitoring, and troubleshooting machine learning models
  • Machine learning terminology (e.g., features, labels, models, regression, classification, recommendation, supervised and unsupervised learning, evaluation metrics)
  • Impact of dependencies of machine learning models
  • Common sources of error (e.g., assumptions about data)

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

The following will be discussed here:

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

Who should take the Google Professional Data Engineer exam

Individuals should pursue the exam if they want to demonstrate their expertise and ability to design and develop Data Engineering. Following professional get benefited from Google Professional Data Engineer Certification

  • Data architects
  • Data engineers
  • Developers responsible for managing big data transformation initiatives
  • Data analysts
  • Data scientists
  • Business analysts

Certification Path

The Google Professional Data Engineer Certification is one of the highest level of certification mainly focussing to the professional Data Engineering.

There is no prerequisite for this exam but still it would be best to follow some sequence in order to prove immense knowledge as a Google professional Data Engineer.

You can complete Google Associate Certifications then approach for the professional certification. For more information related to Google cloud certification track Google-certification-path

What is the duration, language, and format of Google Professional Data Engineer Exam

  • Format: Multiple choices, multiple answers
  • Length of Examination: 120 minutes
  • Number of Questions: 50-60
  • Passing score: 80%
  • Language: English (U.S.), Japanese, Spanish, and Portuguese
  • Cost: $200

How to book Google Professional Data Engineer Exams

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

  • Step1: Visit the Google Cloud Webassessor Website
  • Step2: Sign in or sign up to your Google Cloud Webassessor account
  • 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.

Google Professional Data Engineer Certified Professional salary

The average salary of a Google Professional Data Engineer Certified Expert in

  • United State - 151,247 USD
  • India - 25,42,327 INR
  • Europe - 135,347 EURO
  • England - 115,632 POUND

The benefit of obtaining the Google Professional Data Engineer Exam Certification

A Professional Data Engineer enables data-driven decision making by collecting, transforming, and publishing data. A data engineer should be able to design, build, operationalize, secure, and monitor data processing systems with a particular emphasis on security and compliance; scalability and efficiency; reliability and fidelity; and flexibility and portability. A data engineer should also be able to leverage, deploy, and continuously train pre-existing machine learning models.

Difficulty in Attempting Google Professional Data Engineer Exam Certification

If the user has successfully passed the professional-data-engineer practice exam and has been through professional-data-engineer dumps then the certification exam will not be too much difficult as the user has shown aptitude for understanding complicated processes.

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