Google Professional-Data-Engineer Dumps
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Professional-Data-Engineer FAQs
The Google Professional Data Engineer Exam is a certification test designed to validate your ability to design, build, operationalize, secure, and monitor data processing systems on Google Cloud Platform (GCP).
The Google Professional Data Engineer focuses on GCP services and data engineering practices within Google's ecosystem, while the AWS Certified Big Data – Specialty focuses on big data services and solutions on Amazon Web Services.
The Professional Data Engineer Exam focuses on data engineering tasks such as data processing, machine learning, and data pipelines, while the Associate Cloud Engineer Exam covers foundational skills in deploying and managing applications on GCP.
Google does not publish a specific passing score. Instead, the results are based on a scaled scoring system. Candidates should focus on thoroughly understanding the exam objectives.
The certification can enhance your professional credibility, demonstrate your expertise in data engineering on GCP, and make you a more competitive candidate for advanced data engineering roles.
With this certification, you can pursue roles such as Data Engineer, Data Architect, Machine Learning Engineer, and Big Data Engineer.
Salaries vary based on experience and location, but professionals with this certification can expect to earn between $100,000 and $150,000 per year, with potential for higher earnings in senior roles.
Long-term benefits include career advancement opportunities, higher earning potential, recognition as an expert in data engineering, and the ability to work on cutting-edge data projects.
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Google Professional Data Engineer Exam Questions and Answers
Flowlogistic’s CEO wants to gain rapid insight into their customer base so his sales team can be better informed in the field. This team is not very technical, so they’ve purchased a visualization tool to simplify the creation of BigQuery reports. However, they’ve been overwhelmed by all thedata in the table, and are spending a lot of money on queries trying to find the data they need. You want to solve their problem in the most cost-effective way. What should you do?
Flowlogistic is rolling out their real-time inventory tracking system. The tracking devices will all send package-tracking messages, which will now go to a single Google Cloud Pub/Sub topic instead of the Apache Kafka cluster. A subscriber application will then process the messages for real-time reporting and store them in Google BigQuery for historical analysis. You want to ensure the package data can be analyzed over time.
Which approach should you take?
Flowlogistic’s management has determined that the current Apache Kafka servers cannot handle the data volume for their real-time inventory tracking system. You need to build a new system on Google Cloud Platform (GCP) that will feed the proprietary tracking software. The system must be able to ingest data from a variety of global sources, process and query in real-time, and store the data reliably. Which combination of GCP products should you choose?
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