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Google Data Engineer Sample Questions:

1. Each analytics team in your organization is running BigQuery jobs in their own projects. You want to enable each team to monitor slot usage within their projects. What should you do?

A) Create a Stackdriver Monitoring dashboard based on the BigQuery metric slots/allocated_for_project
B) Create an aggregated log export at the organization level, capture the BigQuery job execution logs, create a custom metric based on the totalSlotMs, and create a Stackdriver Monitoring dashboard based on the custom metric
C) Create a Stackdriver Monitoring dashboard based on the BigQuery metric query/scanned_bytes
D) Create a log export for each project, capture the BigQuery job execution logs, create a custom metric based on the totalSlotMs, and create a Stackdriver Monitoring dashboard based on the custom metric

2. You have a data pipeline with a Cloud Dataflow job that aggregates and writes time series metrics to Cloud Bigtable. This data feeds a dashboard used by thousands of users across the organization. You need to support additional concurrent users and reduce the amount of time required to write the dat a. Which two actions should you take? (Choose two.)

A) Modify your Cloud Dataflow pipeline to use the CoGroupByKey transform before writing to Cloud Bigtable
B) Modify your Cloud Dataflow pipeline to use the Flatten transform before writing to Cloud Bigtable
C) Configure your Cloud Dataflow pipeline to use local execution
D) Increase the number of nodes in the Cloud Bigtable cluster
E) Increase the maximum number of Cloud Dataflow workers by setting maxNumWorkers in PipelineOptions

3. What is the general recommendation when designing your row keys for a Cloud Bigtable schema?

A) Keep your row key as long as the field permits
B) Include multiple time series values within the row key
C) Keep your row key reasonably short
D) Keep the row keep as an 8 bit integer

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

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

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

Solutions: