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Google Data Engineer Sample Questions:
1. What is the general recommendation when designing your row keys for a Cloud Bigtable schema?
A) Include multiple time series values within the row key
B) Keep your row key reasonably short
C) Keep the row keep as an 8 bit integer
D) Keep your row key as long as the field permits
2. You want to migrate an on-premises Hadoop system to Cloud Dataproc. Hive is the primary tool in use, and the data format is Optimized Row Columnar (ORC). All ORC files have been successfully copied to a Cloud Storage bucket. You need to replicate some data to the cluster's local Hadoop Distributed File System (HDFS) to maximize performance. What are two ways to start using Hive in Cloud Dataproc? (Choose two.)
A) Leverage Cloud Storage connector for Hadoop to mount the ORC files as external Hive table
B) Load the ORC files into BigQuer
C) Replicate external Hive tables to the native ones.
D) Replicate external Hive tables to the native ones.
E) Mount the Hive tables from HDFS.
F) Run the gsutil utility to transfer all ORC files from the Cloud Storage bucket to any node of the Dataproc cluste
G) Leverage BigQuery connector for Hadoop to mount the BigQuery tables as external Hive table
H) Mount the Hive tables locally.
I) Run the gsutil utility to transfer all ORC files from the Cloud Storage bucket to HDF
J) Then run the Hadoop utility to copy them do HDF
K) Mount the Hive tables locally.
L) Run the gsutil utility to transfer all ORC files from the Cloud Storage bucket to the master node of the Dataproc cluste
3. You want to process payment transactions in a point-of-sale application that will run on Google Cloud Platform. Your user base could grow exponentially, but you do not want to manage infrastructure scaling.
Which Google database service should you use?
A) Cloud SQL
B) Cloud Datastore
C) BigQuery
D) Cloud Bigtable
4. Which of these sources can you not load data into BigQuery from?
A) Google Cloud Storage
B) File upload
C) Google Cloud SQL
D) Google Drive
5. You are integrating one of your internal IT applications and Google BigQuery, so users can query BigQuery from the application's interface. You do not want individual users to authenticate to BigQuery and you do not want to give them access to the dataset. You need to securely access BigQuery from your IT application.
What should you do?
A) Use the service account's private key to access the dataset
B) Integrate with a single sign-on (SSO) platform, and pass each user's credentials along with the query request
C) Store the username and password for that user in a file on the files system, and use those credentials to access the BigQuery dataset
D) Create a dummy user and grant dataset access to that use
E) Create a service account and grant dataset access to that accoun
F) Create groups for your users and give those groups access to the dataset
Solutions:
| Question # 1 Answer: B | Question # 2 Answer: F,K | Question # 3 Answer: A | Question # 4 Answer: C | Question # 5 Answer: E |




