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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 query/scanned_bytes
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 slots/allocated_for_project
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 stored in BigQuery. The data in the BigQuery dataset must be highly available. You need to define a storage, backup, and recovery strategy of this data that minimizes cost. How should you configure the BigQuery table?

A) Set the BigQuery dataset to be regiona
B) In the event of an emergency, use the backup copy of the table.
C) Set the BigQuery dataset to be multi-regional
D) Create a scheduled query to make copies of the data to tables suffixed with the time of the backup
E) Create a scheduled query to make copies of the data to tables suffixed with the time of the backu
F) Set the BigQuery dataset to be multi-regional
G) In the event of an emergency, use a point-in-time snapshot to recover the data.
H) In the event of an emergency, use a point-in-time snapshot to recover the data.
I) In the event of an emergency, use the backup copy of the table.
J) Set the BigQuery dataset to be regiona

3. You are building a new application that you need to collect data from in a scalable way. Data arrives continuously from the application throughout the day, and you expect to generate approximately 150 GB of JSON data per day by the end of the year. Your requirements are: Decoupling producer from consumer Space and cost-efficient storage of the raw ingested data, which is to be stored indefinitely Near real-time SQL query Maintain at least 2 years of historical data, which will be queried with SQ Which pipeline should you use to meet these requirements?

A) Write a tool to poll the API and write data to Cloud Storage as gzipped JSON files.
B) Create an application that provides an AP
C) Create an application that publishes events to Cloud Pub/Sub, and create Spark jobs on Cloud Dataproc to convert the JSON data to Avro format, stored on HDFS on Persistent Disk.
D) Create an application that writes to a Cloud SQL database to store the dat
E) Set up periodic exports of the database to write to Cloud Storage and load into BigQuery.
F) Create an application that publishes events to Cloud Pub/Sub, and create a Cloud Dataflow pipeline that transforms the JSON event payloads to Avro, writing the data to Cloud Storage and BigQuery.

4. If you're running a performance test that depends upon Cloud Bigtable, all the choices except one below are recommended steps. Which is NOT a recommended step to follow?

A) Before you test, run a heavy pre-test for several minutes.
B) Run your test for at least 10 minutes.
C) Use at least 300 GB of data.
D) Do not use a production instance.

5. You work for a mid-sized enterprise that needs to move its operational system transaction data from an on-premises database to GCP. The database is about 20 TB in size. Which database should you choose?

A) Cloud Spanner
B) Cloud SQL
C) Cloud Bigtable
D) Cloud Datastore

Solutions: