Cisco®
CompTIA
Checkpoint
ISC
EC-Council
EXIN
Fortinet
ITIL®
Juniper
PMI
Microsoft
VMware
Avaya
Google
Salesforce
Amazon
Palo Alto Networks
ISC2
Splunk®
Scrum
All Vendors
  2. Home
  3. Exams
  4. Google
  5. Professional Data Engineer

Free Professional Data Engineer Exam Braindumps (page: 36)

Page 35 of 95
PDF with 375 Questions

You have enabled the free integration between Firebase Analytics and Google BigQuery. Firebase now automatically creates a new table daily in BigQuery in the format app_events_YYYYMMDD. You want to query all of the tables for the past 30 days in legacy SQL.
What should you do?

  1. Use the TABLE_DATE_RANGE function
  2. Use the WHERE_PARTITIONTIME pseudo column
  3. Use WHERE date BETWEEN YYYY-MM-DD AND YYYY-MM-DD
  4. Use SELECT IF.(date >= YYYY-MM-DD AND date <= YYYY-MM-DD

Answer(s): A


Reference:

https://cloud.google.com/blog/products/gcp/using-bigquery-and-firebase-analytics-to- understandyour-mobile-app?hl=am



Your company is currently setting up data pipelines for their campaign. For all the Google Cloud Pub/Sub streaming data, one of the important business requirements is to be able to periodically identify the inputs and their timings during their campaign. Engineers have decided to use windowing and transformation in Google Cloud Dataflow for this purpose. However, when testing this feature, they find that the Cloud Dataflow job fails for the all streaming insert.
What is the most likely cause of this problem?

  1. They have not assigned the timestamp, which causes the job to fail
  2. They have not set the triggers to accommodate the data coming in late, which causes the job to fail
  3. They have not applied a global windowing function, which causes the job to fail when the pipeline is created
  4. They have not applied a non-global windowing function, which causes the job to fail when the pipeline is created

Answer(s): C



You architect a system to analyze seismic dat

  1. Your extract, transform, and load (ETL) process runs as a series of MapReduce jobs on an Apache Hadoop cluster. The ETL process takes days to process a data set because some steps are computationally expensive. Then you discover that a sensor calibration step has been omitted. How should you change your ETL process to carry out sensor calibration systematically in the future?
  2. Modify the transformMapReduce jobs to apply sensor calibration before they do anything else.
  3. Introduce a new MapReduce job to apply sensor calibration to raw data, and ensure all other MapReduce jobs are chained after this.
  4. Add sensor calibration data to the output of the ETL process, and document that all users need to apply sensor calibration themselves.
  5. Develop an algorithm through simulation to predict variance of data output from the last MapReduce job based on calibration factors, and apply the correction to all data.

Answer(s): A



An online retailer has built their current application on Google App Engine. A new initiative at the company mandates that they extend their application to allow their customers to transact directly via the application.

They need to manage their shopping transactions and analyze combined data from multiple datasets using a business intelligence (BI) tool. They want to use only a single database for this purpose.
Which Google Cloud database should they choose?

  1. BigQuery
  2. Cloud SQL
  3. Cloud BigTable
  4. Cloud Datastore

Answer(s): C


Reference:

https://cloud.google.com/solutions/business-intelligence/






Post your Comments and Discuss Google Professional Data Engineer exam with other Community members:

Exam Discussions & Posts