VALID VERIFIED PROFESSIONAL-DATA-ENGINEER ANSWERS OFFER YOU ACCURATE EXAM PREP | GOOGLE GOOGLE CERTIFIED PROFESSIONAL DATA ENGINEER EXAM

Valid Verified Professional-Data-Engineer Answers offer you accurate Exam Prep | Google Google Certified Professional Data Engineer Exam

Valid Verified Professional-Data-Engineer Answers offer you accurate Exam Prep | Google Google Certified Professional Data Engineer Exam

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Exam Topics

The syllabus of the Google Professional Data Engineer exam is divided into 4 topics, each covering specific knowledge and skills that the candidates need to develop while preparing for the test. A full outline of the exam content can be viewed on the official website. The highlights of the domains covered in the test are as follows:

Topic 1. Designing Data Processing Systems

To answer the questions related to this first topic of the certification exam, the individuals need to demonstrate their proficiency in selecting the proper storage technologies. This includes their understanding of data modeling, schema design, distributed systems, as well as tradeoffs involving throughput, latency, and transactions. Moreover, the applicants need to have the ability to map storage systems to the business needs. It also measures one’s skills in designing data pipelines, designing a data processing solution, as well as migrating data warehousing & data processing.

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To become a Google Professional-Data-Engineer, candidates need to pass the certification exam. Professional-Data-Engineer exam consists of multiple-choice and scenario-based questions that assess a candidate's ability to design, build, and manage data processing systems on the Google Cloud Platform. Professional-Data-Engineer Exam can be taken online or in-person at a proctored testing center. Candidates have two hours to complete the exam, and they must score at least 70% to pass.

Google Certified Professional Data Engineer Exam Sample Questions (Q55-Q60):

NEW QUESTION # 55
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. Deploy small Kafka clusters in your data centers to buffer events.
  • D. Have the data acquisition devices publish data to Cloud Pub/Sub.

Answer: D


NEW QUESTION # 56
The _________ for Cloud Bigtable makes it possible to use Cloud Bigtable in a Cloud Dataflow pipeline.

  • A. BigQuery Data Transfer Service
  • B. DataFlow SDK
  • C. BiqQuery API
  • D. Cloud Dataflow connector

Answer: D

Explanation:
The Cloud Dataflow connector for Cloud Bigtable makes it possible to use Cloud Bigtable in a Cloud Dataflow pipeline. You can use the connector for both batch and streaming operations.


NEW QUESTION # 57
You want to process payment transactions in a point-of-sale application that will run on Google Cloud Platform. Your user base could grow exponentially, but you do not want to manage infrastructure scaling.
Which Google database service should you use?

  • A. Cloud Bigtable
  • B. BigQuery
  • C. Cloud SQL
  • D. Cloud Datastore

Answer: D

Explanation:
https://cloud.google.com/datastore/docs/concepts/overview


NEW QUESTION # 58
Business owners at your company have given you a database of bank transactions. Each row contains the user ID, transaction type, transaction location, and transaction amount. They ask you to investigate what type of machine learning can be applied to the dat

  • A. Unsupervised learning to determine which transactions are most likely to be fraudulent.
  • B. Supervised learning to determine which transactions are most likely to be fraudulent.
  • C. Supervised learning to predict the location of a transaction.
  • D. Unsupervised learning to predict the location of a transaction.
  • E. Which three machine learning applications can you use? (Choose three.)
  • F. Reinforcement learning to predict the location of a transaction.
  • G. Clustering to divide the transactions into N categories based on feature similarity.

Answer: A,B,C


NEW QUESTION # 59
You are designing a messaging system by using Pub/Sub to process clickstream data with an event-driven consumer app that relies on a push subscription. You need to configure the messaging system that is reliable enough to handle temporary downtime of the consumer app. You also need the messaging system to store the input messages that cannot be consumed by the subscriber. The system needs to retry failed messages gradually, avoiding overloading the consumer app, and store the failed messages after a maximum of 10 retries in a topic. How should you configure the Pub/Sub subscription?

  • A. Use exponential backoff as the subscription retry policy, and configure dead lettering to a different topic with maximum delivery attempts set to 10.
  • B. Use immediate redelivery as the subscription retry policy, and configure dead lettering to a different topic with maximum delivery attempts set to 10.
  • C. Increase the acknowledgement deadline to 10 minutes.
  • D. Use exponential backoff as the subscription retry policy, and configure dead lettering to the same source topic with maximum delivery attempts set to 10.

Answer: A


NEW QUESTION # 60
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