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Google GCP-DE Exam Syllabus Topics:

Google Data Engineer Sample Questions:

1. You are designing the database schema for a machine learning-based food ordering service that will predict what users want to eat. Here is some of the information you need to store:
The user profile: What the user likes and doesn't like to eat
The user account information: Name, address, preferred meal times
The order information: When orders are made, from where, to whom
The database will be used to store all the transactional data of the product. You want to optimize the data schem a. Which Google Cloud Platform product should you use?

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

2. You work for an economic consulting firm that helps companies identify economic trends as they happen. As part of your analysis, you use Google BigQuery to correlate customer data with the average prices of the 100 most common goods sold, including bread, gasoline, milk, and others. The average prices of these goods are updated every 30 minutes. You want to make sure this data stays up to date so you can combine it with other data in BigQuery as cheaply as possible. What should you do?

A) Use Cloud Dataflow to query BigQuery and combine the data programmatically with the data stored in Google Cloud Storage.
B) Load the data every 30 minutes into a new partitioned table in BigQuery.
C) Store the data in Google Cloud Datastor
D) Store the data in a file in a regional Google Cloud Storage bucke
E) Store and update the data in a regional Google Cloud Storage bucket and create a federated data source in BigQuery
F) Use Google Cloud Dataflow to query BigQuery and combine the data programmatically with the data stored in Cloud Datastore

3. You are designing storage for two relational tables that are part of a 10-TB database on Google Cloud. You want to support transactions that scale horizontally. You also want to optimize data for range queries on nonkey columns. What should you do?

A) Use Cloud SQL for storag
B) Use Cloud Spanner for storag
C) Use Cloud Dataflow to transform data to support query patterns.
D) Add secondary indexes to support query patterns.
E) Use Cloud Dataflow to transform data to support query patterns.
F) Use Cloud Spanner for storag
G) Add secondary indexes to support query patterns.
H) Use Cloud SQL for storag

4. Each analytics team in your organization is running BigQuery jobs in their own projects. You want to enable each team to monitor slot usage within their projects. What should you do?

A) Create a Stackdriver Monitoring dashboard based on the BigQuery metric query/scanned_bytes
B) Create a Stackdriver Monitoring dashboard based on the BigQuery metric slots/allocated_for_project
C) Create an aggregated log export at the organization level, capture the BigQuery job execution logs, create a custom metric based on the totalSlotMs, and create a Stackdriver Monitoring dashboard based on the custom metric
D) Create a log export for each project, capture the BigQuery job execution logs, create a custom metric based on the totalSlotMs, and create a Stackdriver Monitoring dashboard based on the custom metric

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

Solutions: