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Google Data Engineer Sample Questions:

1. Which is not a valid reason for poor Cloud Bigtable performance?

A) There are issues with the network connection.
B) The Cloud Bigtable cluster has too many nodes.
C) The workload isn't appropriate for Cloud Bigtable.
D) The table's schema is not designed correctly.

2. What are the minimum permissions needed for a service account used with Google Dataproc?

A) Execute to Google Cloud Storage; execute to Google Cloud Logging
B) Execute to Google Cloud Storage; write to Google Cloud Logging
C) Write to Google Cloud Storage; read to Google Cloud Logging
D) Read and write to Google Cloud Storage; write to Google Cloud Logging

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

4. MJTelco needs you to create a schema in Google Bigtable that will allow for the historical analysis of the last 2 years of records. Each record that comes in is sent every 15 minutes, and contains a unique identifier of the device and a data record. The most common query is for all the data for a given device for a given day. Which schema should you use?

A) Rowkey: dateColumn data: device_id, data_point
B) Rowkey: date#data_pointColumn data: device_id
C) Rowkey: date#device_idColumn data: data_point
D) Rowkey: data_pointColumn data: device_id, date
E) Rowkey: device_idColumn data: date, data_point

5. Flowlogistic's CEO wants to gain rapid insight into their customer base so his sales team can be better informed in the field. This team is not very technical, so they've purchased a visualization tool to simplify the creation of BigQuery reports. However, they've been overwhelmed by all the data in the table, and are spending a lot of money on queries trying to find the data they need. You want to solve their problem in the most cost-effective way. What should you do?

A) Export the data into a Google Sheet for virtualization.
B) Create identity and access management (IAM) roles on the appropriate columns, so only they appear in a query.
C) Create a view on the table to present to the virtualization tool.
D) Create an additional table with only the necessary columns.

Solutions: