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Google Data Engineer Sample Questions:

1. 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) Add a new column called FullName to the Users tabl
B) Use BigQuery to export the data for the table to a CSV fil
C) Create a view in BigQuery that concatenates the FirstName and LastName field values to produce the FullName.
D) Run an UPDATE statement that updates the FullName column for each user with the concatenation of the FirstName and LastName values.
E) 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.
F) 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.

2. Which of these is not a supported method of putting data into a partitioned table?

A) If you have existing data in a separate file for each day, then create a partitioned table and upload each file into the appropriate partition.
B) Use ORDER BY to put a table's rows into chronological order and then change the table's type to "Partitioned".
C) Create a partitioned table and stream new records to it every day.
D) Run a query to get the records for a specific day from an existing table and for the destination table, specify a partitioned table ending with the day in the format "$YYYYMMDD".

3. You work for a shipping company that uses handheld scanners to read shipping labels. Your company has strict data privacy standards that require scanners to only transmit recipients' personally identifiable information (PII) to analytics systems, which violates user privacy rules. You want to quickly build a scalable solution using cloud-native managed services to prevent exposure of PII to the analytics systems. What should you do?

A) Install a third-party data validation tool on Compute Engine virtual machines to check the incoming data for sensitive information.
B) Use the tagging and confidence levels to either pass or quarantine the data in a bucket for review.
C) Build a Cloud Function that reads the topics and makes a call to the Cloud Data Loss Prevention AP
D) Use Stackdriver logging to analyze the data passed through the total pipeline to identify transactions that may contain sensitive information.
E) Create an authorized view in BigQuery to restrict access to tables with sensitive data.

4. For the best possible performance, what is the recommended zone for your Compute Engine instance and Cloud Bigtable instance?

A) Have both the Compute Engine instance and the Cloud Bigtable instance to be in different zones.
B) Have both the Compute Engine instance and the Cloud Bigtable instance to be in the same zone.
C) Have the Cloud Bigtable instance to be in the same zone as all of the consumers of your data.
D) Have the Compute Engine instance in the furthest zone from the Cloud Bigtable instance.

5. You are running a pipeline in Cloud Dataflow that receives messages from a Cloud Pub/Sub topic and writes the results to a BigQuery dataset in the EU. Currently, your pipeline is located in europe-west4 and has a maximum of 3 workers, instance type n1-standard-1. You notice that during peak periods, your pipeline is struggling to process records in a timely fashion, when all 3 workers are at maximum CPU utilization. Which two actions can you take to increase performance of your pipeline? (Choose two.)

A) Increase the number of max workers
B) Create a temporary table in Cloud Bigtable that will act as a buffer for new data
C) Create a temporary table in Cloud Spanner that will act as a buffer for new data
D) Change the zone of your Cloud Dataflow pipeline to run in us-central1
E) Create a new step in your pipeline to write to this table first, and then create a new pipeline to write from Cloud Bigtable to BigQuery
F) Create a new step in your pipeline to write to this table first, and then create a new pipeline to write from Cloud Spanner to BigQuery
G) Use a larger instance type for your Cloud Dataflow workers

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