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Google Data Engineer Sample Questions:

1. Which of the following is NOT one of the three main types of triggers that Dataflow supports?

A) Trigger based on element size in bytes
B) Trigger that is a combination of other triggers
C) Trigger based on time
D) Trigger based on element count

2. MJTelco is building a custom interface to share dat
a. They have these requirements:
They need to do aggregations over their petabyte-scale datasets.
They need to scan specific time range rows with a very fast response time (milliseconds). Which combination of Google Cloud Platform products should you recommend?

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

3. When a Cloud Bigtable node fails, is lost.

A) the last transaction
B) all data
C) the time dimension
D) no data

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

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

Solutions: