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Google GCP-DE Exam Syllabus Topics:

Google Data Engineer Sample Questions:

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

Question 2

Which Cloud Dataflow / Beam feature should you use to aggregate data in an unbounded data source every hour based on the time when the data entered the pipeline?

A. A processing time trigger
B. An hourly watermark
C. The with Allowed Lateness method
D. An event time trigger

Question 3

You work for a large fast food restaurant chain with over 400,000 employees. You store employee information in Google BigQuery in a Users table consisting of a FirstName field and a LastName field. A member of IT is building an application and asks you to modify the schema and data in BigQuery so the application can query a FullName field consisting of the value of the FirstName field concatenated with a space, followed by the value of the LastName field for each employee. How can you make that data available while minimizing cost?

A. Create a Google Cloud Dataproc job to process the CSV file and output a new CSV file containing the proper values for FirstName, LastName and FullNam G. Run a BigQuery load job to load the new CSV file into BigQuery.
B. Use BigQuery to export the data for the table to a CSV fil
C. Add a new column called FullName to the Users tabl
D. Run an UPDATE statement that updates the FullName column for each user with the concatenation of the FirstName and LastName values.
E. Create a Google Cloud Dataflow job that queries BigQuery for the entire Users table, concatenates the FirstName value and LastName value for each user, and loads the proper values for FirstName, LastName, and FullName into a new table in BigQuery.
F. Create a view in BigQuery that concatenates the FirstName and LastName field values to produce the FullName.

Question 4

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

Question 5

You have developed three data processing jobs. One executes a Cloud Dataflow pipeline that transforms data uploaded to Cloud Storage and writes results to BigQuery. The second ingests data from on-premises servers and uploads it to Cloud Storage. The third is a Cloud Dataflow pipeline that gets information from third-party data providers and uploads the information to Cloud Storage. You need to be able to schedule and monitor the execution of these three workflows and manually execute them when needed. What should you do?

A. Create a Direct Acyclic Graph in Cloud Composer to schedule and monitor the jobs.
B. Set up cron jobs in a Compute Engine instance to schedule and monitor the pipelines using GCP API calls.
C. Use Stackdriver Monitoring and set up an alert with a Webhook notification to trigger the jobs.
D. Develop an App Engine application to schedule and request the status of the jobs using GCP API calls.

Solutions: