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.
While there is no strict requirement, Google recommends at least 3+ years of industry experience, including 1+ year of hands-on work with Google Cloud. This helps ensure you can handle scenario-based questions and design decisions effectively.
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Google Professional Data Engineer Exam Questions and Answers
You have a data processing application that runs on Google Kubernetes Engine (GKE). Containers need to be launched with their latest available configurations from a container registry. Your GKE nodes need to have GPUs. local SSDs, and 8 Gbps bandwidth. You want to efficiently provision the data processing infrastructure and manage the deployment process. What should you do?
You are designing the architecture to process your data from Cloud Storage to BigQuery by using Dataflow. The network team provided you with the Shared VPC network and subnetwork to be used by your pipelines. You need to enable the deployment of the pipeline on the Shared VPC network. What should you do?
Your company's customer_order table in BigOuery stores the order history for 10 million customers, with a table size of 10 PB. You need to create a dashboard for the support team to view the order history. The dashboard has two filters, countryname and username. Both are string data types in the BigQuery table. When a filter is applied, the dashboard fetches the order history from the table and displays the query results. However, the dashboard is slow to show the results when applying the filters to the following query:
How should you redesign the BigQuery table to support faster access?
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