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Google Data Engineer Sample Questions:

1. Which row keys are likely to cause a disproportionate number of reads and/or writes on a particular node in a Bigtable cluster (select 2 answers)?

A) A timestamp followed by a stock symbol
B) A non-sequential numeric ID
C) A stock symbol followed by a timestamp
D) A sequential numeric ID

2. Which of these rules apply when you add preemptible workers to a Dataproc cluster (select 2 answers)?

A) If a preemptible worker is reclaimed, then a replacement worker must be added manually.
B) Preemptible workers cannot use persistent disk.
C) Preemptible workers cannot store data.
D) A Dataproc cluster cannot have only preemptible workers.

3. You are building a new data pipeline to share data between two different types of applications: jobs generators and job runners. Your solution must scale to accommodate increases in usage and must accommodate the addition of new applications without negatively affecting the performance of existing ones. What should you do?

A) Create a table on Cloud SQL, and insert and delete rows with the job information
B) Use a Cloud Pub/Sub topic to publish jobs, and use subscriptions to execute them
C) Create a table on Cloud Spanner, and insert and delete rows with the job information
D) Create an API using App Engine to receive and send messages to the applications

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

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

Solutions: