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Google GCP-DE Exam Syllabus Topics:

Google Data Engineer Sample Questions:

1. Flowlogistic's management has determined that the current Apache Kafka servers cannot handle the data volume for their real-time inventory tracking system. You need to build a new system on Google Cloud Platform (GCP) that will feed the proprietary tracking software. The system must be able to ingest data from a variety of global sources, process and query in real-time, and store the data reliably. Which combination of GCP products should you choose?

A) Cloud Pub/Sub, Cloud Dataflow, and Cloud Storage
B) Cloud Pub/Sub, Cloud Dataflow, and Local SSD
C) Cloud Pub/Sub, Cloud SQL, and Cloud Storage
D) Cloud Load Balancing, Cloud Dataflow, and Cloud Storage

2. An online retailer has built their current application on Google App Engine. A new initiative at the company mandates that they extend their application to allow their customers to transact directly via the application.
They need to manage their shopping transactions and analyze combined data from multiple datasets using a business intelligence (BI) tool. They want to use only a single database for this purpose. Which Google Cloud database should they choose?

A) Cloud Datastore
B) Cloud BigTable
C) BigQuery
D) Cloud SQL

3. 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) Partition the tables by a column containing a TIMESTAMP or DATE Type.
B) Recommend that the Data Science team use wildcards on the table name suffixes to select the data they need.
C) Re-create the tables using DD
D) 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.
E) 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.
F) Write an Apache Beam pipeline that creates a BigQuery table per data

4. You receive data files in CSV format monthly from a third party. You need to cleanse this data, but every third month the schema of the files changes. Your requirements for implementing these transformations include:
Executing the transformations on a schedule
Enabling non-developer analysts to modify transformations
Providing a graphical tool for designing transformations
What should you do?

A) Merge the transformed tables together with a SQL query
B) Use Cloud Dataprep to build and maintain the transformation recipes, and execute them on a scheduled basis
C) Help the analysts write a Cloud Dataflow pipeline in Python to perform the transformatio
D) Load each month's CSV data into BigQuery, and write a SQL query to transform the data to a standard scheme
E) Use Apache Spark on Cloud Dataproc to infer the schema of the CSV file before creating a Dataframe.Then implement the transformations in Spark SQL before writing the data out to Cloud Storage and loading into BigQuery
F) The Python code should be stored in a revision control system and modified as the incoming data's schema changes

5. 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) Create an App Engine cron job to schedule the execution of the Cloud Dataprep job.
C) Create a cron schedule in Cloud Dataprep.
D) Export the recipe as a Cloud Dataprep template, and create a job in Cloud Scheduler.

Solutions: