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Free Professional Data Engineer Exam Braindumps (page: 23)

Page 14 of 95
PDF with 373 Questions

You work for an economic consulting firm that helps companies identify economic trends as they happen. As part of your analysis, you use Google BigQuery to correlate customer data with the average prices of the 100 most common goods sold, including bread, gasoline, milk, and others. The average prices of these goods are updated every 30 minutes. You want to make sure this data stays up to date so you can combine it with other data in BigQuery as cheaply as possible.
What should you do?

  1. Load the data every 30 minutes into a new partitioned table in BigQuery.
  2. Store and update the data in a regional Google Cloud Storage bucket and create a federated data source in BigQuery
  3. Store the data in Google Cloud Datastore. Use Google Cloud Dataflow to query BigQuery and combine the data programmatically with the data stored in Cloud Datastore
  4. Store the data in a file in a regional Google Cloud Storage bucket. Use Cloud Dataflow to query BigQuery and combine the data programmatically with the data stored in Google Cloud Storage.

Answer(s): A



You are designing the database schema for a machine learning-based food ordering service that will predict what users want to eat. Here is some of the information you need to store:

The user profile: What the user likes and doesn't like to eat

The user account information: Name, address, preferred meal times

The order information: When orders are made, from where, to whom

The database will be used to store all the transactional data of the product. You want to optimize the data schem

  1. Which Google Cloud Platform product should you use?
  2. BigQuery
  3. Cloud SQL
  4. Cloud Bigtable
  5. Cloud Datastore

Answer(s): A



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?

  1. The CSV data loaded in BigQuery is not flagged as CSV.
  2. The CSV data has invalid rows that were skipped on import.
  3. The CSV data loaded in BigQuery is not using BigQuery's default encoding.
  4. The CSV data has not gone through an ETL phase before loading into BigQuery.

Answer(s): B



Your company produces 20,000 files every hour. Each data file is formatted as a comma separated values (CSV) file that is less than 4 KB. All files must be ingested on Google Cloud Platform before they can be processed. Your company site has a 200 ms latency to Google Cloud, and your Internet connection bandwidth is limited as 50 Mbps. You currently deploy a secure FTP (SFTP) server on a virtual machine in Google Compute Engine as the data ingestion point. A local SFTP client runs on a dedicated machine to transmit the CSV files as is. The goal is to make reports with data from the previous day available to the executives by 10:00 a.m. each day. This design is barely able to keep up with the current volume, even though the bandwidth utilization is rather low.

You are told that due to seasonality, your company expects the number of files to double for the next three months.
Which two actions should you take? (choose two.)

  1. Introduce data compression for each file to increase the rate file of file transfer.
  2. Contact your internet service provider (ISP) to increase your maximum bandwidth to at least 100 Mbps.
  3. Redesign the data ingestion process to use gsutil tool to send the CSV files to a storage bucket in parallel.
  4. Assemble 1,000 files into a tape archive (TAR) file. Transmit the TAR files instead, and disassemble the CSV files in the cloud upon receiving them.
  5. Create an S3-compatible storage endpoint in your network, and use Google Cloud Storage Transfer Service to transfer on-premices data to the designated storage bucket.

Answer(s): C,E






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