Google Data Engineer : GCP-DE

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

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

3. What are the minimum permissions needed for a service account used with Google Dataproc?

A) Write to Google Cloud Storage; read to Google Cloud Logging
B) Execute to Google Cloud Storage; write to Google Cloud Logging
C) Read and write to Google Cloud Storage; write to Google Cloud Logging
D) Execute to Google Cloud Storage; execute to Google Cloud Logging

4. Your company maintains a hybrid deployment with GCP, where analytics are performed on your anonymized customer dat a. The data are imported to Cloud Storage from your data center through parallel uploads to a data transfer server running on GCP. Management informs you that the daily transfers take too long and have asked you to fix the problem. You want to maximize transfer speeds. Which action should you take?

A) Increase your network bandwidth from Compute Engine to Cloud Storage.
B) Increase your network bandwidth from your datacenter to GCP.
C) Increase the size of the Google Persistent Disk on your server.
D) Increase the CPU size on your server.

5. You need to create a data pipeline that copies time-series transaction data so that it can be queried from within BigQuery by your data science team for analysis. Every hour, thousands of transactions are updated with a new status. The size of the intitial dataset is 1.5 PB, and it will grow by 3 TB per day. The data is heavily structured, and your data science team will build machine learning models based on this dat a. You want to maximize performance and usability for your data science team. Which two strategies should you adopt? Choose 2 answers.

A) Use BigQuery'ssupport for external data sources to query.
B) Denormalize the data as must as possible.
C) Develop a data pipeline where status updates are appended to BigQuery instead of updated.
D) Preserve the structure of the data as much as possible.
E) Use BigQuery UPDATE to further reduce the size of the dataset.
F) Copy a daily snapshot of transaction data to Cloud Storage and store it as an Avro fil

Solutions: