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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

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.

According to Google, a Google Certified Professional - Data Engineer “enables data-driven decision making by collecting, transforming, and visualizing 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.

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.

In this guide, we will cover the Google Professional Data Engineer Exam, Google Professional Data Engineer Certified professionals salary and all aspects of the Google Professional Data Engineer Certification.

Introduction to Google Professional Data Engineer Exam

Google Professional Data Engineer Examis a certification exam that is conducted by Google to validates candidate knowledge and skills of working as a Professional Data Engineer in the IT industry.

The Google Cloud Professional Data Engineer is able to harness the power of Google's big data capabilities and make data-driven decisions by collecting, transforming, and visualizing data. Through designing, building, maintaining, and troubleshooting data processing systems with a particular emphasis on the security, reliability, fault-tolerance, scalability, fidelity, and efficiency of such systems, a Google Cloud data engineer is able to put these systems to work.

After passing this exam, candidates get a certificate from Google that helps them to demonstrate their proficiency in Google Professional Data Engineer to their clients and employers.

Topics of Google Professional Data Engineer Exam

Candidates must know the exam topics before they start of preparation. Because It will really help them in hitting the core. Our Google Professional Data Engineer Dumps will include the following topics:

1. 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)

Designing a data processing solution

  • 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

Migrating data warehousing and data processing

  • 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

2. 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

3. 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)

4. Ensuring solution quality

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 Google Professional Data Engineer 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

How to study the Google Professional Data Engineer Exam

Preparation of certification exams could be covered with two resource types . The first one are the study guides, reference books and study forums that are elaborated and appropriate for building information from ground up. Apart from them video tutorials and lectures are a good option to ease the pain of through study and are relatively make the study process more interesting nonetheless these demand time and concentration from the learner. Smart candidates who wish to create a solid foundation altogether examination topics and connected technologies typically mix video lectures with study guides to reap the advantages of each but practice exams or practice exam engines is one important study tool which goes typically unnoted by most candidates. Practice exams are designed with our experts to make exam prospects test their knowledge on skills attained in course, as well as prospects become comfortable and familiar with the real exam environment. Statistics have indicated exam anxiety plays much bigger role of students failure in exam than the fear of the unknown. Certification-questions Expert Team recommends preparing some notes on these topics along with it don't forget to practice Google Professional Data Engineer Exam dumps which had been written by our Experts Team, each of these can assist you loads to clear this exam with excellent marks.

Google Professional Data Engineer 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

How much Google Professional Data Engineer Exam cost

Google Professional Data Engineer exam cost is $200 USD (plus tax where applicable)

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.

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: Google Professional Data Engineer offered in English (U.S.), Japanese, Spanish, and Portuguese
  • Recommended experience: 3+ years of industry experience including 1+ years designing and managing solutions using GCP

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

  • Google Professional Data Engineer Certification is distinguished among competitors. Google Professional Data Engineer certification can give them an edge at that time easily when candidates appear for employment interview, employers are very fascinated to note one thing that differentiates the individual from all other candidates.
  • Google Professional Data Engineer certification has more useful and relevant networks that help them in setting career goals for themselves. Google Professional Data Engineer networks provide them with the correct career guidance than non certified generally are unable to get.
  • Google Professional Data Engineer will be confident and stand different from others as their skills are more trained than non-certified professionals.
  • Google Professional Data Engineer Exam provide proven knowledge to use the tools to complete the task efficiently and cost effectively than the other non-certified professionals lack in doing so.
  • Google Professional Data Engineer Certification provides practical experience to candidates from all the aspects to be a proficient worker in the organization.
  • Google Professional Data Engineer Certifications provide opportunities to get a job easily in which they are interested in instead of wasting years and ending without getting any experience.

Difficulty in writing the Google Professional Data Engineer Exam

Google Professional Data Engineer Certification is a most privileged achievement one could be graced with. It is one of the highest level of certification in the Google. This Certification consisting of real time scenarios and practical experience which make it difficult for the candidate to get through with the Google Professional Data Engineer Exam. If the candidates have proper preparation material to pass the Google Professional Data Engineer exam with good grades. Questions answers and clarifications which are designed in form of Certification-questions dumps make sure to cover entire course content. Certification-questions have a brilliant Google Professional Data Engineer dumps with the foremost latest and vital queries and answers in PDF format. Certification-questions is sure about the exactness and legitimacy of Google Professional Data Engineer exam dumps and in this manner. Candidates can easily pass the Google Professional Data Engineer exam with genuine Google Professional Data Engineer dumps and get Google Professional Data Engineer certification skillful surely.
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