PROFESSIONAL-DATA-ENGINEER TESTDUMP, PROFESSIONAL-DATA-ENGINEER VALID EXAM CAMP PDF

Professional-Data-Engineer Testdump, Professional-Data-Engineer Valid Exam Camp Pdf

Professional-Data-Engineer Testdump, Professional-Data-Engineer Valid Exam Camp Pdf

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Google Certified Professional Data Engineer exam is a comprehensive test that evaluates the candidate's proficiency in a variety of areas related to data processing systems. Professional-Data-Engineer exam covers topics such as the design and implementation of data processing systems, the management of data storage and processing systems, and the optimization of data processing workflows. Professional-Data-Engineer exam is designed to be challenging and requires a deep understanding of data processing systems and the Google Cloud Platform.

Google Professional-Data-Engineer Exam is a certification offered by Google to validate the skills and expertise of data engineers. Professional-Data-Engineer exam is designed to test the ability of professionals to design, build, and maintain data processing systems that meet the needs of their organizations. It covers a wide range of topics, including data processing architecture, data modeling, data ingestion, and transformation.

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Professional-Data-Engineer Valid Exam Camp Pdf, Valid Professional-Data-Engineer Test Dumps

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Google Professional-Data-Engineer Certification Exam is a highly recognized certification program that validates the knowledge and skills of professionals in the field of data engineering. Google Certified Professional Data Engineer Exam certification is designed to demonstrate the ability of data engineers to design, build, and maintain data processing systems, as well as to troubleshoot and optimize them for performance and cost-effectiveness. Google Certified Professional Data Engineer Exam certification exam covers a range of topics, including data processing systems, data storage and management, data analysis and machine learning, and security and compliance.

Google Certified Professional Data Engineer Exam Sample Questions (Q259-Q264):

NEW QUESTION # 259
Which action can a Cloud Dataproc Viewer perform?

  • A. Submit a job.
  • B. Delete a cluster.
  • C. Create a cluster.
  • D. List the jobs.

Answer: D

Explanation:
A Cloud Dataproc Viewer is limited in its actions based on its role. A viewer can only list clusters, get cluster details, list jobs, get job details, list operations, and get operation details.
Reference:
https://cloud.google.com/dataproc/docs/concepts/iam#iam_roles_and_cloud_dataproc_ope rations_summary


NEW QUESTION # 260
You operate a logistics company, and you want to improve event delivery reliability for vehicle-based sensors.
You operate small data centers around the world to capture these events, but leased lines that provide connectivity from your event collection infrastructure to your event processing infrastructure are unreliable, with unpredictable latency. You want to address this issue in the most cost-effective way. What should you do?

  • A. Establish a Cloud Interconnect between all remote data centers and Google.
  • B. Write a Cloud Dataflow pipeline that aggregates all data in session windows.
  • C. Have the data acquisition devices publish data to Cloud Pub/Sub.
  • D. Deploy small Kafka clusters in your data centers to buffer events.

Answer: D


NEW QUESTION # 261
Which SQL keyword can be used to reduce the number of columns processed by BigQuery?

  • A. WHERE
  • B. SELECT
  • C. LIMIT
  • D. BETWEEN

Answer: B

Explanation:
SELECT allows you to query specific columns rather than the whole table.
LIMIT, BETWEEN, and WHERE clauses will not reduce the number of columns processed by BigQuery.
Reference: https://cloud.google.com/bigquery/launch-
checklist#architecture_design_and_development_checklist


NEW QUESTION # 262
You created an analytics environment on Google Cloud so that your data scientist team can explore data without impacting the on-premises Apache Hadoop solution. The data in the on-premises Hadoop Distributed File System (HDFS) cluster is in Optimized Row Columnar (ORC) formatted files with multiple columns of Hive partitioning. The data scientist team needs to be able to explore the data in a similar way as they used the on-premises HDFS cluster with SQL on the Hive query engine. You need to choose the most cost-effective storage and processing solution. What should you do?

  • A. Import the ORC files lo Bigtable tables for the data scientist team.
  • B. Copy the ORC files on Cloud Storage, then create external BigQuery tables for the data scientist team.
  • C. Copy the ORC files on Cloud Storage, then deploy a Dataproc cluster for the data scientist team.
  • D. Import the ORC files to BigOuery tables for the data scientist team.

Answer: B


NEW QUESTION # 263
You are designing a real-time system for a ride hailing app that identifies areas with high demand for rides to effectively reroute available drivers to meet the demand. The system ingests data from multiple sources to Pub
/Sub. processes the data, and stores the results for visualization and analysis in real-time dashboards. The data sources include driver location updates every 5 seconds and app-based booking events from riders. The data processing involves real-time aggregation of supply and demand data for the last 30 seconds, every 2 seconds, and storing the results in a low-latency system for visualization. What should you do?

  • A. Group the data by using a session window in a Dataflow pipeline, and write the aggregated data to BigQuery.
  • B. Group the data by using a hopping window in a Dataflow pipeline, and write the aggregated data to BigQuery.
  • C. Group the data by using a hopping window in a Dataflow pipeline, and write the aggregated data to Memorystore
  • D. Group the data by using a tumbling window in a Dataflow pipeline, and write the aggregated data to Memorystore

Answer: C

Explanation:
A hopping window is a type of sliding window that advances by a fixed period of time, producing overlapping windows. This is suitable for the scenario where the system needs to aggregate data for the last
30 seconds, every 2 seconds, and provide real-time updates. A Dataflow pipeline can implement the hopping window logic using Apache Beam, and process both streaming and batch data sources. Memorystore is a low- latency, in-memory data store that can serve the aggregated data to the visualization layer. BigQuery is not a good choice for this scenario, as it is not optimized for low-latency queries and frequent updates.


NEW QUESTION # 264
......

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