Professional Data Engineer Practice Test

Professional Data Engineer Practice Test practice questions, page 12 of 14, with answer review and explanations.

Question 56

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

Question 57

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

Question 58

You architect a system to analyze seismic data. 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?

  1. Modify the transformMapReduce jobs to apply sensor calibration before they do anything else.
  2. Introduce a new MapReduce job to apply sensor calibration to raw data, and ensure all other MapReduce jobs are chained after this.
  3. Add sensor calibration data to the output of the ETL process, and document that all users need to apply sensor calibration themselves.
  4. 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.

Question 59

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

Question 60

You launched a new gaming app almost three years ago. You have been uploading log files from the previous day to a separate Google BigQuery table with the table name format LOGS_yyyymmdd. You have been using table wildcard functions to generate daily and monthly reports for all time ranges. Recently, you discovered that some queries that cover long date ranges are exceeding the limit of 1,000 tables and failing. How can you resolve this issue?

  1. Convert all daily log tables into date-partitioned tables
  2. Convert the sharded tables into a single partitioned table
  3. Enable query caching so you can cache data from previous months
  4. Create separate views to cover each month, and query from these views