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

2. You are developing a software application using Google's Dataflow SDK, and want to use conditional, for loops and other complex programming structures to create a branching pipeline. Which component will be used for the data processing operation?

A) Pipeline
B) PCollection
C) Transform
D) Sink API

3. You want to migrate an on-premises Hadoop system to Cloud Dataproc. Hive is the primary tool in use, and the data format is Optimized Row Columnar (ORC). All ORC files have been successfully copied to a Cloud Storage bucket. You need to replicate some data to the cluster's local Hadoop Distributed File System (HDFS) to maximize performance. What are two ways to start using Hive in Cloud Dataproc? (Choose two.)

A) Run the gsutil utility to transfer all ORC files from the Cloud Storage bucket to HDF
B) Leverage Cloud Storage connector for Hadoop to mount the ORC files as external Hive table
C) Replicate external Hive tables to the native ones.
D) Mount the Hive tables locally.
E) Run the gsutil utility to transfer all ORC files from the Cloud Storage bucket to any node of the Dataproc cluste
F) Leverage BigQuery connector for Hadoop to mount the BigQuery tables as external Hive table
G) Run the gsutil utility to transfer all ORC files from the Cloud Storage bucket to the master node of the Dataproc cluste
H) Replicate external Hive tables to the native ones.
I) Load the ORC files into BigQuer
J) Mount the Hive tables from HDFS.
K) Mount the Hive tables locally.
L) Then run the Hadoop utility to copy them do HDF

4. What Dataflow concept determines when a Window's contents should be output based on certain criteria being met?

A) Sessions
B) OutputCriteria
C) Triggers
D) Windows

5. You are selecting services to write and transform JSON messages from Cloud Pub/Sub to BigQuery for a data pipeline on Google Cloud. You want to minimize service costs. You also want to monitor and accommodate input data volume that will vary in size with minimal manual intervention. What should you do?

A) Use the default autoscaling setting for worker instances.
B) Monitor the total execution time for a sampling of job
C) Locate the bottleneck and adjust cluster resources.
D) Use Cloud Dataproc to run your transformation
E) Monitor the job system lag with Stackdrive
F) Monitor CPU utilization for the cluste
G) Use Cloud Dataflow to run your transformation
H) Use the diagnose command to generate an operational output archiv
I) Resize the number of worker nodes in your cluster via the command line.
J) Use Cloud Dataproc to run your transformation
K) Use Cloud Dataflow to run your transformation
L) Configure the job to use non-default Compute Engine machine types when needed.

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