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Update Exam: Google Professional Data Engineer
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<h3><strong>Introduction to Google Professional Data Engineer Exam</strong></h3><p>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 an aspirant meets Google’s proficiency standards.</p><p>The Professional Data Engineer exam assesses your ability to:</p><ul><li>Design data processing systems</li><li>Build and operationalize data processing systems</li><li>Operationalize machine learning models</li><li>Ensure solution quality</li></ul><p>Google Professional Data Engineer Exam certification is evidence of your skills, and expertise in those areas in which you like to work. If a candidate wants to work as a Google Professional Data Engineer and prove his knowledge, Certification is offered by Google. This Google Professional Data Engineer Certification helps a candidate to validate his skills in Big Data and Data Engineering Technology.</p><h4 style="margin-left:0px;"><strong>What is the duration, language, and format of the Google Professional Data Engineer Exam</strong></h4><ul><li>Format: Multiple choices, multiple answers</li><li>Length of Examination: 120 minutes</li><li>Number of Questions: 50-60</li><li>Passing score: 80%</li><li>Language: English (U.S.), Japanese, Spanish, and Portuguese</li><li>Cost: $200</li></ul><h4 style="margin-left:0px;"><strong>How to Book Google Professional Data Engineer Exams</strong></h4><p>The registration for the Google Professional Data Engineer Exam follows the steps given below.</p><ul><li>Step 1: Visit the Google Cloud Website</li><li>Step 2: Sign in or sign up to your Google Cloud Webassessor account</li><li>Step 3: Search for the exam name Google Professional Data Engineer</li><li>Step 4: Take the date of the exam, choose the exam centre and make further payment using payment methods like credit/debit etc.</li></ul><h4 style="margin-left:0px;"><strong>Google Professional Data Engineer Certified Professional salary</strong></h4><p>The average salary of a Google Professional Data Engineer Certified Expert in</p><ul><li>United States - 151,247 USD</li><li>India - 25,42,327 INR</li><li>Europe - 135,347 EURO</li><li>England - 115,632 POUND</li></ul><h4 style="margin-left:0px;"><strong>Who should take the Google Professional Data Engineer exam</strong></h4><p>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</p><ul><li>Data architects</li><li>Data engineers</li><li>Developers responsible for managing big data transformation initiatives</li><li>Data analysts</li><li>Data scientists</li><li>Business analysts</li></ul><h4 style="margin-left:0px;"><strong>Certification Path</strong></h4><p>The Google Professional Data Engineer Certification is one of the highest level of certification mainly focussing to the professional Data Engineering.</p><p>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.</p><p>You can complete Google Associate Certifications then approach for the professional certification. For more information related to Google cloud certification track </p><h4 style="margin-left:0px;"><strong>The benefit of obtaining the Google Professional Data Engineer Exam Certification</strong></h4><p>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.</p><p><br> </p>
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