VALID PROFESSIONAL-DATA-ENGINEER BRAINDUMPS - PROFESSIONAL-DATA-ENGINEER VALID TEST FEE

Valid Professional-Data-Engineer Braindumps - Professional-Data-Engineer Valid Test Fee

Valid Professional-Data-Engineer Braindumps - Professional-Data-Engineer Valid Test Fee

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Google Professional-Data-Engineer Certification Exam is a rigorous and comprehensive exam that requires individuals to have a deep understanding of data engineering technologies and concepts. Professional-Data-Engineer exam consists of multiple choice and scenario-based questions that assess an individual's ability to design, build, and maintain data processing systems on Google Cloud Platform. Professional-Data-Engineer exam is timed and individuals have a limited amount of time to complete the exam. To pass the exam, individuals must score 70% or higher.

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Google Certified Professional Data Engineer Exam Sample Questions (Q107-Q112):

NEW QUESTION # 107
Your company is loading comma-separated values (CSV) files into Google BigQuery. The data is fully
imported successfully; however, the imported data is not matching byte-to-byte to the source file. What is
the most likely cause of this problem?

  • A. The CSV data has not gone through an ETL phase before loading into BigQuery.
  • B. The CSV data loaded in BigQuery is not flagged as CSV.
  • C. The CSV data has invalid rows that were skipped on import.
  • D. The CSV data loaded in BigQuery is not using BigQuery's default encoding.

Answer: C


NEW QUESTION # 108
Your company is using WILDCARD tables to query data across multiple tables with similar names. The SQL statement is currently failing with the following error:
# Syntax error : Expected end of statement but got "-" at [4:11]
SELECT age
FROM
bigquery-public-data.noaa_gsod.gsod
WHERE
age != 99
AND_TABLE_SUFFIX = '1929'
ORDER BY
age DESC
Which table name will make the SQL statement work correctly?

  • A. 'bigquery-public-data.noaa_gsod.gsod*`
  • B. 'bigquery-public-data.noaa_gsod.gsod'
  • C. bigquery-public-data.noaa_gsod.gsod*
  • D. 'bigquery-public-data.noaa_gsod.gsod'*

Answer: A


NEW QUESTION # 109
You are designing a pipeline that publishes application events to a Pub/Sub topic. Although message ordering is not important, you need to be able to aggregate events across disjoint hourly intervals before loading the results to BigQuery for analysis. What technology should you use to process and load this data to BigQuery while ensuring that it will scale with large volumes of events?

  • 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. Schedule a Cloud Function to run hourly, pulling all available messages from the Pub/Sub topic and performing the necessary aggregations.
  • C. Schedule a batch Dataflow job to run hourly, pulling all available messages from the Pub/Sub topic and performing the necessary aggregations.
  • D. Create a streaming Dataflow job that reads continually from the Pub/Sub topic and performs aggregations using tumbling windows.

Answer: A

Explanation:
Explanation/Reference:


NEW QUESTION # 110
You have a BigQuery dataset named "customers". All tables will be tagged by using a Data Catalog tag template named "gdpr". The template contains one mandatory field, "has sensitive data~. with a boolean value. All employees must be able to do a simple search and find tables in the dataset that have either true or false in the "has sensitive data" field. However, only the Human Resources (HR) group should be able to see the data inside the tables for which "hass-ensitive-data" is true. You give the all employees group the bigquery.metadataViewer and bigquery.connectionUser roles on the dataset. You want to minimize configuration overhead. What should you do next?

  • A. Create the "gdpr" tag template with private visibility. Assign the bigquery -dataViewer role to the HR group on the tables that contain sensitive data.
  • B. Create the ~gdpr" tag template with private visibility. Assign the datacatalog. tagTemplateViewer role on this tag to the all employees group, and assign the bigquery.dataViewer role to the HR group on the tables that contain sensitive data.
  • C. Create the "gdpr" tag template with public visibility. Assign the datacatalog. tagTemplateViewer role on this tag to the all employees.group, and assign the bijquery.dataViewer role to the HR group on the tables that contain sensitive data.
  • D. Create the "gdpr" tag template with public visibility. Assign the bigquery. dataViewer role to the HR group on the tables that contain sensitive data.

Answer: C

Explanation:
To ensure that all employees can search and find tables with GDPR tags while restricting data access to sensitive tables only to the HR group, follow these steps:
Data Catalog Tag Template:
Use Data Catalog to create a tag template named "gdpr" with a boolean field "has sensitive data". Set the visibility to public so all employees can see the tags.
Roles and Permissions:
Assign the datacatalog.tagTemplateViewer role to the all employees group. This role allows users to view the tags and search for tables based on the "has sensitive data" field.
Assign the bigquery.dataViewer role to the HR group specifically on tables that contain sensitive data. This ensures only HR can access the actual data in these tables.
Steps to Implement:
Create the GDPR Tag Template:
Define the tag template in Data Catalog with the necessary fields and set visibility to public.
Assign Roles:
Grant the datacatalog.tagTemplateViewer role to the all employees group for visibility into the tags.
Grant the bigquery.dataViewer role to the HR group on tables marked as having sensitive data.
Reference:
Data Catalog Documentation
Managing Access Control in BigQuery
IAM Roles in Data Catalog


NEW QUESTION # 111
Your company is streaming real-time sensor data from their factory floor into Bigtable and they have
noticed extremely poor performance. How should the row key be redesigned to improve Bigtable
performance on queries that populate real-time dashboards?

  • A. Use a row key of the form >#<sensorid>#<timestamp>.
  • B. Use a row key of the form <sensorid>.
  • C. Use a row key of the form <timestamp>#<sensorid>.
  • D. Use a row key of the form <timestamp>.

Answer: D


NEW QUESTION # 112
......

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