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Google Data Engineer Sample Questions:

1. When a Cloud Bigtable node fails, is lost.

A) the last transaction
B) all data
C) the time dimension
D) no data

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

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

4. You are running a pipeline in Cloud Dataflow that receives messages from a Cloud Pub/Sub topic and writes the results to a BigQuery dataset in the EU. Currently, your pipeline is located in europe-west4 and has a maximum of 3 workers, instance type n1-standard-1. You notice that during peak periods, your pipeline is struggling to process records in a timely fashion, when all 3 workers are at maximum CPU utilization. Which two actions can you take to increase performance of your pipeline? (Choose two.)

A) Change the zone of your Cloud Dataflow pipeline to run in us-central1
B) Create a temporary table in Cloud Spanner that will act as a buffer for new data
C) Increase the number of max workers
D) Create a new step in your pipeline to write to this table first, and then create a new pipeline to write from Cloud Bigtable to BigQuery
E) Create a temporary table in Cloud Bigtable that will act as a buffer for new data
F) Create a new step in your pipeline to write to this table first, and then create a new pipeline to write from Cloud Spanner to BigQuery
G) Use a larger instance type for your Cloud Dataflow workers

5. You are implementing several batch jobs that must be executed on a schedule. These jobs have many interdependent steps that must be executed in a specific order. Portions of the jobs involve executing shell scripts, running Hadoop jobs, and running queries in BigQuery. The jobs are expected to run for many minutes up to several hours. If the steps fail, they must be retried a fixed number of times. Which service should you use to manage the execution of these jobs?

A) Cloud Dataflow
B) Cloud Composer
C) Cloud Scheduler
D) Cloud Functions

Solutions: