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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 a Stackdriver Monitoring dashboard based on the BigQuery metric slots/allocated_for_project
C) 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
D) 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

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

3. Which row keys are likely to cause a disproportionate number of reads and/or writes on a particular node in a Bigtable cluster (select 2 answers)?

A) A stock symbol followed by a timestamp
B) A timestamp followed by a stock symbol
C) A sequential numeric ID
D) A non-sequential numeric ID

4. You operate an IoT pipeline built around Apache Kafka that normally receives around 5000 messages per second. You want to use Google Cloud Platform to create an alert as soon as the moving average over 1 hour drops below 4000 messages per second. What should you do?

A) Use Cloud Scheduler to run a script every five minutes that counts the number of rows created in BigQuery in the last hour
B) Consume the stream of data in Cloud Dataflow using Kafka I
C) Use Kafka Connect to link your Kafka message queue to Cloud Pub/Su
D) Use a Cloud Dataflow template to write your messages from Cloud Pub/Sub to Cloud Bigtabl
E) If that number falls below 4000, send an alert.
F) If that number falls below 4000, send an alert.
G) Set a fixed time window of 1 hour.Compute the average when the window closes, and send an alert if the average is less than 4000 messages.
H) Compute the average when the window closes, and send an alert if the average is less than 4000 messages.
I) Consume the stream of data in Cloud Dataflow using Kafka I
J) Use Kafka Connect to link your Kafka message queue to Cloud Pub/Su
K) Use a Cloud Dataflow template to write your messages from Cloud Pub/Sub to BigQuer
L) Set a sliding time window of 1 hour every 5 minute
M) Use Cloud Scheduler to run a script every hour that counts the number of rows created in Cloud Bigtable in the last hour

5. You work for a manufacturing company that sources up to 750 different components, each from a different supplier. You've collected a labeled dataset that has on average 1000 examples for each unique component. Your team wants to implement an app to help warehouse workers recognize incoming components based on a photo of the component. You want to implement the first working version of this app (as Proof-Of-Concept) within a few working days. What should you do?

A) Use Cloud Vision AutoML with the existing dataset.
B) Use Cloud Vision AutoML, but reduce your dataset twice.
C) Use Cloud Vision API by providing custom labels as recognition hints.
D) Train your own image recognition model leveraging transfer learning techniques.

Solutions: