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Google Data Engineer Sample Questions:

1. 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) Mount the Hive tables from HDFS.
B) Replicate external Hive tables to the native ones.
C) Mount the Hive tables locally.
D) Replicate external Hive tables to the native ones.
E) Run the gsutil utility to transfer all ORC files from the Cloud Storage bucket to the master node of the Dataproc cluste
F) Run the gsutil utility to transfer all ORC files from the Cloud Storage bucket to HDF
G) Run the gsutil utility to transfer all ORC files from the Cloud Storage bucket to any node of the Dataproc cluste
H) Leverage Cloud Storage connector for Hadoop to mount the ORC files as external Hive table
I) Load the ORC files into BigQuer
J) Mount the Hive tables locally.
K) Leverage BigQuery connector for Hadoop to mount the BigQuery tables as external Hive table
L) Then run the Hadoop utility to copy them do HDF

2. You are developing an application on Google Cloud that will automatically generate subject labels for users' blog posts. You are under competitive pressure to add this feature quickly, and you have no additional developer resources. No one on your team has experience with machine learning. What should you do?

A) Call the model from your application and process the results as labels.
B) Build and train a text classification model using TensorFlo
C) Deploy the model using a KubernetesEngine cluste
D) Process the generated Sentiment Analysis as labels.
E) Call the Cloud Natural Language API from your applicatio
F) Build and train a text classification model using TensorFlo
G) Deploy the model using Cloud Machine Learning Engin
H) Call the Cloud Natural Language API from your applicatio
I) Call the model from your application and process the results as labels.
J) Process the generated Entity Analysis as labels.

3. You are a retailer that wants to integrate your online sales capabilities with different in-home assistants, such as Google Home. You need to interpret customer voice commands and issue an order to the backend systems. Which solutions should you choose?

A) Dialogflow Enterprise Edition
B) Cloud AutoML Natural Language
C) Cloud Natural Language API
D) Cloud Speech-to-Text API

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

5. Which of these is not a supported method of putting data into a partitioned table?

A) Use ORDER BY to put a table's rows into chronological order and then change the table's type to "Partitioned".
B) Run a query to get the records for a specific day from an existing table and for the destination table, specify a partitioned table ending with the day in the format "$YYYYMMDD".
C) Create a partitioned table and stream new records to it every day.
D) If you have existing data in a separate file for each day, then create a partitioned table and upload each file into the appropriate partition.

Solutions: