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Google Data Engineer Sample Questions:

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

2. You are building a model to predict whether or not it will rain on a given day. You have thousands of input features and want to see if you can improve training speed by removing some features while having a minimum effect on model accuracy. What can you do?

A) Eliminate features that are highly correlated to the output labels.
B) Combine highly co-dependent features into one representative feature.
C) Remove the features that have null values for more than 50% of the training records.
D) Instead of feeding in each feature individually, average their values in batches of 3.

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

4. Your company maintains a hybrid deployment with GCP, where analytics are performed on your anonymized customer dat a. The data are imported to Cloud Storage from your data center through parallel uploads to a data transfer server running on GCP. Management informs you that the daily transfers take too long and have asked you to fix the problem. You want to maximize transfer speeds. Which action should you take?

A) Increase your network bandwidth from Compute Engine to Cloud Storage.
B) Increase your network bandwidth from your datacenter to GCP.
C) Increase the size of the Google Persistent Disk on your server.
D) Increase the CPU size on your server.

5. You want to process payment transactions in a point-of-sale application that will run on Google Cloud Platform. Your user base could grow exponentially, but you do not want to manage infrastructure scaling.
Which Google database service should you use?

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

Solutions: