FREE PDF QUIZ 2025 PROFESSIONAL-DATA-ENGINEER: HIGH PASS-RATE AUTHENTIC GOOGLE CERTIFIED PROFESSIONAL DATA ENGINEER EXAM EXAM QUESTIONS

Free PDF Quiz 2025 Professional-Data-Engineer: High Pass-Rate Authentic Google Certified Professional Data Engineer Exam Exam Questions

Free PDF Quiz 2025 Professional-Data-Engineer: High Pass-Rate Authentic Google Certified Professional Data Engineer Exam Exam Questions

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Google Professional-Data-Engineer exam is designed to test an individual's ability to design, build, and maintain data processing systems on Google Cloud Platform. Professional-Data-Engineer exam is intended for data engineers, developers, and other IT professionals who are responsible for designing and implementing data solutions on Google Cloud Platform. Professional-Data-Engineer exam covers a broad range of topics, including data processing, data warehousing, data analysis, and machine learning.

The Google Professional-Data-Engineer exam covers a wide range of topics, including data processing, storage, analysis, transformation, and visualization on Google Cloud Platform. Candidates are expected to have a deep understanding of Google Cloud Platform services and tools, as well as the ability to design and implement scalable, reliable, and efficient data processing systems that meet business requirements. Google Certified Professional Data Engineer Exam certification exam is rigorous and challenging, requiring candidates to demonstrate their ability to apply their knowledge and skills to real-world scenarios. Successful candidates will be able to demonstrate their proficiency in designing and building data processing systems on Google Cloud Platform and will be recognized as experts in this field.

Google Professional-Data-Engineer (Google Certified Professional Data Engineer) Exam is a certification program that validates the skills of a data engineer in designing, building, and managing data processing systems. It is designed to assess the proficiency of a data engineer in using Google Cloud technologies to develop and deploy scalable, reliable, and efficient data pipelines. Professional-Data-Engineer exam covers various topics, including data processing architectures, data modeling, data analysis, and machine learning.

Google Certified Professional Data Engineer Exam Sample Questions (Q58-Q63):

NEW QUESTION # 58
Which software libraries are supported by Cloud Machine Learning Engine?

  • A. Theano and Torch
  • B. TensorFlow
  • C. Theano and TensorFlow
  • D. TensorFlow and Torch

Answer: B

Explanation:
Cloud ML Engine mainly does two things:
Enables you to train machine learning models at scale by running TensorFlow training applications in the cloud.
Hosts those trained models for you in the cloud so that you can use them to get predictions about new data.
Reference: https://cloud.google.com/ml-engine/docs/technical-overview#what_it_does


NEW QUESTION # 59
You are collecting loT sensor data from millions of devices across the world and storing the data in BigQuery. Your access pattern is based on recent data tittered by location_id and device_version with the following query:

You want to optimize your queries for cost and performance. How should you structure your data?

  • A. Cluster table data by create_date, partition by location and device_version
  • B. Cluster table data by create_date location_id and device_version
  • C. Partition table data by create_date cluster table data by tocation_id and device_version
  • D. Partition table data by create_date, location_id and device_version

Answer: B


NEW QUESTION # 60
Your company uses a proprietary system to send inventory data every 6 hours to a data ingestion service in the cloud. Transmitted data includes a payload of several fields and the timestamp of the transmission. If there are any concerns about a transmission, the system re-transmits the data. How should you deduplicate the data most efficiency?

  • A. Compute the hash value of each data entry, and compare it with all historical data.
  • B. Store each data entry as the primary key in a separate database and apply an index.
  • C. Maintain a database table to store the hash value and other metadata for each data entry.
  • D. Assign global unique identifiers (GUID) to each data entry.

Answer: C


NEW QUESTION # 61
Your weather app queries a database every 15 minutes to get the current temperature. The frontend is powered by Google App Engine and server millions of users. How should you design the frontend to respond to a database failure?

  • A. Retry the query with exponential backoff, up to a cap of 15 minutes.
  • B. Retry the query every second until it comes back online to minimize staleness of data.
  • C. Reduce the query frequency to once every hour until the database comes back online.
  • D. Issue a command to restart the database servers.

Answer: A

Explanation:
Explanation/Reference:


NEW QUESTION # 62
You are designing a pipeline that publishes application events to a Pub/Sub topic. You need to aggregate events across hourly intervals before loading the results to BigQuery for analysis. Your solution must be scalable so it can process and load large volumes of events to BigQuery. What should you do?

  • A. Create a Cloud Function to perform the necessary data processing that executes using the Pub/Sub trigger every time a new message is published to the topic.
  • B. Create a streaming Dataflow job to continually read from the Pub/Sub topic and perform the necessary aggregations using tumbling windows
  • C. Schedule a Cloud Function to run hourly, pulling all avertable messages from the Pub/Sub topic and performing the necessary aggregations
  • D. Schedule a batch Dataflow job to run hourly, pulling all available messages from the Pub-Sub topic and performing the necessary aggregations

Answer: B


NEW QUESTION # 63
......

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