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Google GCP-DE Exam Syllabus Topics:

Google Data Engineer Sample Questions:

1. You have historical data covering the last three years in BigQuery and a data pipeline that delivers new data to BigQuery daily. You have noticed that when the Data Science team runs a query filtered on a date column and limited to 30-90 days of data, the query scans the entire table. You also noticed that your bill is increasing more quickly than you expected. You want to resolve the issue as cost-effectively as possible while maintaining the ability to conduct SQL queries. What should you do?

A) Recommend that the Data Science team export the table to a CSV file on Cloud Storage and use Cloud Datalab to explore the data by reading the files directly.
B) Modify your pipeline to maintain the last 30-90 days of data in one table and the longer history in a different table to minimize full table scans over the entire history.
C) Partition the tables by a column containing a TIMESTAMP or DATE Type.
D) Re-create the tables using DD
E) Write an Apache Beam pipeline that creates a BigQuery table per data
F) Recommend that the Data Science team use wildcards on the table name suffixes to select the data they need.

2. You used Cloud Dataprep to create a recipe on a sample of data in a BigQuery table. You want to reuse this recipe on a daily upload of data with the same schema, after the load job with variable execution time completes. What should you do?

A) Export the Cloud Dataprep job as a Cloud Dataflow template, and incorporate it into a Cloud Composer job.
B) Export the recipe as a Cloud Dataprep template, and create a job in Cloud Scheduler.
C) Create a cron schedule in Cloud Dataprep.
D) Create an App Engine cron job to schedule the execution of the Cloud Dataprep job.

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

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) 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) Add a new column called FullName to the Users tabl
D) 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.
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) Use BigQuery to export the data for the table to a CSV fil

5. Which of the following job types are supported by Cloud Dataproc (select 3 answers)?

A) Hive
B) YARN
C) Pig
D) Spark

Solutions: