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Google Data Engineer Sample Questions:

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

2. You work for a shipping company that uses handheld scanners to read shipping labels. Your company has strict data privacy standards that require scanners to only transmit recipients' personally identifiable information (PII) to analytics systems, which violates user privacy rules. You want to quickly build a scalable solution using cloud-native managed services to prevent exposure of PII to the analytics systems. What should you do?

A) Build a Cloud Function that reads the topics and makes a call to the Cloud Data Loss Prevention AP
B) Install a third-party data validation tool on Compute Engine virtual machines to check the incoming data for sensitive information.
C) Create an authorized view in BigQuery to restrict access to tables with sensitive data.
D) Use the tagging and confidence levels to either pass or quarantine the data in a bucket for review.
E) Use Stackdriver logging to analyze the data passed through the total pipeline to identify transactions that may contain sensitive information.

3. You are planning to use Google's Dataflow SDK to analyze customer data such as displayed below. Your project requirement is to extract only the customer name Passing Certification Exams Made Easy visit - https://www.2PassEasy.com from the data source and then write to an output PCollection.
Tom,555 X street Tim,553 Y street Sam, 111 Z street
Which operation is best suited for the above data processing requirement?

A) Sink API
B) ParDo
C) Data extraction
D) Source API

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

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

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

Solutions: