PROFESSIONAL-DATA-ENGINEER EXAM REVIEWS, DETAILED PROFESSIONAL-DATA-ENGINEER STUDY DUMPS

Professional-Data-Engineer Exam Reviews, Detailed Professional-Data-Engineer Study Dumps

Professional-Data-Engineer Exam Reviews, Detailed Professional-Data-Engineer Study Dumps

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Google Professional-Data-Engineer Exam is a rigorous test of an individual's skills and knowledge in data engineering on Google Cloud technologies. As demand for skilled data engineering professionals continues to grow, the certification can open up many lucrative job opportunities for those looking to make their mark in the industry. Google Certified Professional Data Engineer Exam certification process requires practical experience, extensive preparation, and dedication to acquiring skills that are highly valued in today's rapidly evolving technology ecosystem.

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Google Certified Professional Data Engineer Exam Sample Questions (Q197-Q202):

NEW QUESTION # 197
You want to store your team's shared tables in a single dataset to make data easily accessible to various analysts. You want to make this data readable but unmodifiable by analysts. At the same time, you want to provide the analysts with individual workspaces in the same project, where they can create and store tables for their own use, without the tables being accessible by other analysts. What should you do?

  • A. Give analysts the BigQuery Data Viewer role on the shared dataset. Create a dataset for each analyst, and give each analyst the BigQuery Data Editor role at the dataset level for their assigned dataset
  • B. Give analysts the BigQuery Data Viewer role at the project level Create a dataset for each analyst, and give each analyst the BigQuery Data Editor role at the project level.
  • C. Give analysts the BigQuery Data Viewer role on the shared dataset Create one other dataset and give the analysts the BigQuery Data Editor role on that dataset.
  • D. Give analysts the BigQuery Data Viewer role at the project level Create one other dataset, and give the analysts the BigQuery Data Editor role on that dataset.

Answer: A

Explanation:
The BigQuery Data Viewer role allows users to read data and metadata from tables and views, but not to modify or delete them. By giving analysts this role on the shared dataset, you can ensure that they can access the data for analysis, but not change it. The BigQuery Data Editor role allows users to create, update, and delete tables and views, as well as read and write data. By giving analysts this role at the dataset level for their assigned dataset, you can provide them with individual workspaces where they can store their own tables and views, without affecting the shared dataset or other analysts' datasets. This way, you can achieve both data protection and data isolation for your team. Reference:
BigQuery IAM roles and permissions
Basic roles and permissions


NEW QUESTION # 198
You are building a new data pipeline to share data between two different types of applications: jobs generators and job runners. Your solution must scale to accommodate increases in usage and must accommodate the addition of new applications without negatively affecting the performance of existing ones. What should you do?

  • A. Create a table on Cloud Spanner, and insert and delete rows with the job information
  • B. Use a Cloud Pub/Sub topic to publish jobs, and use subscriptions to execute them
  • C. Create a table on Cloud SQL, and insert and delete rows with the job information
  • D. Create an API using App Engine to receive and send messages to the applications

Answer: D

Explanation:
References:


NEW QUESTION # 199
You have spent a few days loading data from comma-separated values (CSV) files into the Google
BigQuery table CLICK_STREAM. The column DTstores the epoch time of click events. For convenience,
you chose a simple schema where every field is treated as the STRINGtype. Now, you want to compute
web session durations of users who visit your site, and you want to change its data type to the
TIMESTAMP. You want to minimize the migration effort without making future queries computationally
expensive. What should you do?

  • A. Construct a query to return every row of the table CLICK_STREAM, while using the built-in function to
    cast strings from the column DTinto TIMESTAMPvalues. Run the query into a destination table
    NEW_CLICK_STREAM, in which the column TSis the TIMESTAMPtype. Reference the table
    NEW_CLICK_STREAMinstead of the table CLICK_STREAMfrom now on. In the future, new data is
    loaded into the table NEW_CLICK_STREAM.
  • B. Delete the table CLICK_STREAM, and then re-create it such that the column DTis of the TIMESTAMP
    type. Reload the data.
  • C. Add two columns to the table CLICK STREAM: TSof the TIMESTAMPtype and IS_NEWof the
    BOOLEANtype. Reload all data in append mode. For each appended row, set the value of IS_NEWto
    true. For future queries, reference the column TSinstead of the column DT, with the WHEREclause
    ensuring that the value of IS_NEWmust be true.
  • D. Add a column TSof the TIMESTAMPtype to the table CLICK_STREAM, and populate the numeric
    values from the column TSfor each row. Reference the column TSinstead of the column DTfrom now
    on.
  • E. Create a view CLICK_STREAM_V, where strings from the column DTare cast into TIMESTAMPvalues.
    Reference the view CLICK_STREAM_Vinstead of the table CLICK_STREAMfrom now on.

Answer: C


NEW QUESTION # 200
Your business users need a way to clean and prepare data before using the data for analysis. Your business users are less technically savvy and prefer to work with graphical user interfaces to define their transformations. After the data has been transformed, the business users want to perform their analysis directly in a spreadsheet. You need to recommend a solution that they can use. What should you do?

  • A. Use Dataprep to clean the data, and write the results to BigQuery Analyze the data by using Looker Studio.
  • B. Use Dataflow to clean the data, and write the results to BigQuery. Analyze the data by using Connected Sheets.
  • C. Use Dataprep to clean the data, and write the results to BigQuery Analyze the data by using Connected Sheets.
  • D. Use Dataflow to clean the data, and write the results to BigQuery. Analyze the data by using Looker Studio.

Answer: C

Explanation:
For business users who are less technically savvy and prefer graphical user interfaces, Dataprep is an ideal tool for cleaning and preparing data, as it offers a user-friendly interface for defining data transformations without the need for coding. Once the data is cleaned and prepared, writing the results to BigQuery allows for the storage and management of large datasets. Analyzing the data using Connected Sheets enables business users to work within the familiar environment of a spreadsheet, leveraging the power of BigQuery directly within Google Sheets. This solution aligns with the needs of the users and follows Google's recommended practices for data cleaning, preparation, and analysis.
References:
* Connected Sheets | Google Sheets | Google for Developers
* Professional Data Engineer Certification Exam Guide | Learn - Google Cloud
* Engineer Data in Google Cloud | Google Cloud Skills Boost - Qwiklabs


NEW QUESTION # 201
Which SQL keyword can be used to reduce the number of columns processed by BigQuery?

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

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:
http://cloud.google.com/bigquery/launch-checklist#architecture_design_and_development_checklist


NEW QUESTION # 202
......

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