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Google Data Engineer Sample Questions:

1. Suppose you have a table that includes a nested column called "city" inside a column called "person", but when you try to submit the following query in BigQuery, it gives you an error.
SELECT person FROM `project1.example.table1` WHERE city = "London"
How would you correct the error?

A) Change "person" to "city.person".
B) Change "person" to "person.city".
C) Add ", UNNEST(city)" before the WHERE clause.
D) Add ", UNNEST(person)" before the WHERE clause.

2. When using Cloud Dataproc clusters, you can access the YARN web interface by configuring a browser to connect through a proxy.

A) VPN
B) HTTP
C) SOCKS
D) HTTPS

3. You are creating a new pipeline in Google Cloud to stream IoT data from Cloud Pub/Sub through Cloud Dataflow to BigQuery. While previewing the data, you notice that roughly 2% of the data appears to be corrupt. You need to modify the Cloud Dataflow pipeline to filter out this corrupt data. What should you do?

A) Add a SideInput that returns a Boolean if the element is corrupt.
B) Add a Partition transform in Cloud Dataflow to separate valid data from corrupt data.
C) Add a GroupByKey transform in Cloud Dataflow to group all of the valid data together and discard the rest.
D) Add a ParDo transform in Cloud Dataflow to discard corrupt elements.

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

5. As your organization expands its usage of GCP, many teams have started to create their own projects. Projects are further multiplied to accommodate different stages of deployments and target audiences. Each project requires unique access control configurations. The central IT team needs to have access to all projects. Furthermore, data from Cloud Storage buckets and BigQuery datasets must be shared for use in other projects in an ad hoc way. You want to simplify access control management by minimizing the number of policies. Which two steps should you take? Choose 2 answers.

A) Create distinct groups for various teams, and specify groups in Cloud IAM policies.
B) For each Cloud Storage bucket or BigQuery dataset, decide which projects need acces
C) Only use service accounts when sharing data for Cloud Storage buckets and BigQuery datasets.
D) Introduce resource hierarchy to leverage access control policy inheritance.
E) Find all the active members who have access to these projects, and create a Cloud IAM policy to grant access to all these users.
F) Use Cloud Deployment Manager to automate access provision.

Solutions: