LATEST PROFESSIONAL-DATA-ENGINEER DUMPS QUESTIONS - VALID PROFESSIONAL-DATA-ENGINEER EXAM FORUM

Latest Professional-Data-Engineer Dumps Questions - Valid Professional-Data-Engineer Exam Forum

Latest Professional-Data-Engineer Dumps Questions - Valid Professional-Data-Engineer Exam Forum

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Google Professional-Data-Engineer Certification is a valuable credential that can help professionals stand out in the competitive field of data engineering. It demonstrates that the certified individual has the expertise and skills required to design, build, and manage data processing systems effectively. As data becomes increasingly important in organizations of all sizes and industries, the demand for certified data engineers is expected to grow, making this certification a worthwhile investment for individuals looking to advance their career in the field.

Google Professional-Data-Engineer certification exam is a valuable credential for individuals seeking to advance their careers in data engineering and analytics. It validates an individual's knowledge and expertise in Google Cloud technologies and demonstrates their ability to design and implement data solutions that meet business requirements. By passing this certification exam, individuals can enhance their credibility and marketability in the competitive field of data engineering and analytics.

To prepare for the exam, candidates can take advantage of a range of resources, including Google Cloud’s official documentation, online courses, and practice exams. They can also join study groups and attend events and webinars to connect with other professionals and learn from their experiences.

Google Certified Professional Data Engineer Exam Sample Questions (Q11-Q16):

NEW QUESTION # 11
You need to set access to BigQuery for different departments within your company. Your solution should comply with the following requirements:
* Each department should have access only to their data.
* Each department will have one or more leads who need to be able to create and update tables and provide them to their team.
* Each department has data analysts who need to be able to query but not modify data.
How should you set access to the data in BigQuery?

  • A. Create a table for each department. Assign the department leads the role of Owner, and assign the data analysts the role of Editor on the project the table is in.
  • B. Create a dataset for each department. Assign the department leads the role of OWNER, and assign the data analysts the role of WRITER on their dataset.
  • C. Create a table for each department. Assign the department leads the role of Editor, and assign the data analysts the role of Viewer on the project the table is in.
  • D. Create a dataset for each department. Assign the department leads the role of WRITER, and assign the data analysts the role of READER on their dataset.

Answer: C


NEW QUESTION # 12
You are working on a sensitive project involving private user dat
a. You have set up a project on Google Cloud Platform to house your work internally. An external consultant is going to assist with coding a complex transformation in a Google Cloud Dataflow pipeline for your project. How should you maintain users' privacy?

  • A. Create an anonymized sample of the data for the consultant to work with in a different project.
  • B. Grant the consultant the Cloud Dataflow Developer role on the project.
  • C. Create a service account and allow the consultant to log on with it.
  • D. Grant the consultant the Viewer role on the project.

Answer: C


NEW QUESTION # 13
You work for a large fast food restaurant chain with over 400,000 employees. You store employee information in Google BigQuery in a Userstable consisting of a FirstNamefield and a LastNamefield. A member of IT is building an application and asks you to modify the schema and data in BigQuery so the application can query a FullNamefield consisting of the value of the FirstNamefield concatenated with a space, followed by the value of the LastNamefield for each employee. How can you make that data available while minimizing cost?

  • A. Use BigQuery to export the data for the table to a CSV file. Create a Google Cloud Dataproc job to process the CSV file and output a new CSV file containing the proper values for FirstName, LastNameand FullName. Run a BigQuery load job to load the new CSV file into BigQuery.
  • B. Add a new column called FullNameto the Users table. Run an UPDATEstatement that updates the FullNamecolumn for each user with the concatenation of the FirstNameand LastNamevalues.
  • C. Create a view in BigQuery that concatenates the FirstNameand LastNamefield values to produce the FullName.
  • D. Create a Google Cloud Dataflow job that queries BigQuery for the entire Userstable, concatenates the FirstNamevalue and LastNamevalue for each user, and loads the proper values for FirstName, LastName, and FullNameinto a new table in BigQuery.

Answer: D

Explanation:
Explanation/Reference:


NEW QUESTION # 14
Which of the following are feature engineering techniques? (Select 2 answers)

  • A. Hidden feature layers
  • B. Feature prioritization
  • C. Bucketization of a continuous feature
  • D. Crossed feature columns

Answer: C,D

Explanation:
Explanation
Selecting and crafting the right set of feature columns is key to learning an effective model.
Bucketization is a process of dividing the entire range of a continuous feature into a set of consecutive bins/buckets, and then converting the original numerical feature into a bucket ID (as a categorical feature) depending on which bucket that value falls into.
Using each base feature column separately may not be enough to explain the data. To learn the differences between different feature combinations, we can add crossed feature columns to the model.
Reference:
https://www.tensorflow.org/tutorials/wide#selecting_and_engineering_features_for_the_model


NEW QUESTION # 15
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. Have the data acquisition devices publish data to Cloud Pub/Sub.
  • 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. Establish a Cloud Interconnect between all remote data centers and Google.

Answer: A

Explanation:
Pubsub is global service with high message delivery capacity.


NEW QUESTION # 16
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