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Google Data Engineer Sample Questions:

1. The YARN ResourceManager and the HDFS NameNode interfaces are available on a Cloud Dataproc cluster .

A) conditional node
B) application node
C) worker node
D) master node

2. You launched a new gaming app almost three years ago. You have been uploading log files from the previous day to a separate Google BigQuery table with the table name format LOGS_yyyymmdd. You have been using table wildcard functions to generate daily and monthly reports for all time ranges. Recently, you discovered that some queries that cover long date ranges are exceeding the limit of 1,000 tables and failing. How can you resolve this issue?

A) Convert all daily log tables into date-partitioned tables
B) Create separate views to cover each month, and query from these views
C) Convert the sharded tables into a single partitioned table
D) Enable query caching so you can cache data from previous months

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

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

5. What are all of the BigQuery operations that Google charges for?

A) Storage, queries, and exporting data
B) Storage, queries, and loading data from a file
C) Storage, queries, and streaming inserts
D) Queries and streaming inserts

Solutions: