Google GCP-DE

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Google Data Engineer Sample Questions:

1. You are creating a new pipeline in Google Cloud to stream IoT data from Cloud Pub/Sub through Cloud Dataflow to BigQuery. While previewing the data, you notice that roughly 2% of the data appears to be corrupt. You need to modify the Cloud Dataflow pipeline to filter out this corrupt data. What should you do?

A) Add a GroupByKey transform in Cloud Dataflow to group all of the valid data together and discard the rest.
B) Add a Partition transform in Cloud Dataflow to separate valid data from corrupt data.
C) Add a SideInput that returns a Boolean if the element is corrupt.
D) Add a ParDo transform in Cloud Dataflow to discard corrupt elements.

2. Which of the following is NOT one of the three main types of triggers that Dataflow supports?

A) Trigger based on element size in bytes
B) Trigger based on element count
C) Trigger based on time
D) Trigger that is a combination of other triggers

3. 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?

A) Use the WHERE_PARTITIONTIME pseudo column
B) Use SELECT IF.(date >= YYYY-MM-DD AND date <= YYYY-MM-DD
C) Use the TABLE_DATE_RANGE function
D) Use WHERE date BETWEEN YYYY-MM-DD AND YYYY-MM-DD

4. Your company maintains a hybrid deployment with GCP, where analytics are performed on your anonymized customer dat a. The data are imported to Cloud Storage from your data center through parallel uploads to a data transfer server running on GCP. Management informs you that the daily transfers take too long and have asked you to fix the problem. You want to maximize transfer speeds. Which action should you take?

A) Increase the CPU size on your server.
B) Increase the size of the Google Persistent Disk on your server.
C) Increase your network bandwidth from your datacenter to GCP.
D) Increase your network bandwidth from Compute Engine to Cloud Storage.

5. You work for a large fast food restaurant chain with over 400,000 employees. You store employee information in Google BigQuery in a Users table consisting of a FirstName field and a LastName field. A member of IT is building an application and asks you to modify the schema and data in BigQuery so the application can query a FullName field consisting of the value of the FirstName field concatenated with a space, followed by the value of the LastName field for each employee. How can you make that data available while minimizing cost?

A) Create a view in BigQuery that concatenates the FirstName and LastName field values to produce the FullName.
B) Run an UPDATE statement that updates the FullName column for each user with the concatenation of the FirstName and LastName values.
C) Create a Google Cloud Dataflow job that queries BigQuery for the entire Users table, concatenates the FirstName value and LastName value for each user, and loads the proper values for FirstName, LastName, and FullName into a new table in BigQuery.
D) Create a Google Cloud Dataproc job to process the CSV file and output a new CSV file containing the proper values for FirstName, LastName and FullNam G. Run a BigQuery load job to load the new CSV file into BigQuery.
E) Add a new column called FullName to the Users tabl
F) Use BigQuery to export the data for the table to a CSV fil

Solutions: