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Google Data Engineer Sample Questions:

1. You work for a mid-sized enterprise that needs to move its operational system transaction data from an on-premises database to GCP. The database is about 20 TB in size. Which database should you choose?

A) Cloud Spanner
B) Cloud Datastore
C) Cloud Bigtable
D) Cloud SQL

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

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

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) Only use service accounts when sharing data for Cloud Storage buckets and BigQuery datasets.
B) Create distinct groups for various teams, and specify groups in Cloud IAM policies.
C) Introduce resource hierarchy to leverage access control policy inheritance.
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) Use Cloud Deployment Manager to automate access provision.
F) For each Cloud Storage bucket or BigQuery dataset, decide which projects need acces

5. You want to automate execution of a multi-step data pipeline running on Google Cloud. The pipeline includes Cloud Dataproc and Cloud Dataflow jobs that have multiple dependencies on each other. You want to use managed services where possible, and the pipeline will run every day. Which tool should you use?

A) Cloud Scheduler
B) Workflow Templates on Cloud Dataproc
C) Cloud Composer
D) cron

Solutions: