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Google Data Engineer Sample Questions:

1. 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

2. You have enabled the free integration between Firebase Analytics and Google BigQuery. Firebase now automatically creates a new table daily in BigQuery in the format app_events_YYYYMMDD. You want to query all of the tables for the past 30 days in legacy SQL. What should you do?

A) Use WHERE date BETWEEN YYYY-MM-DD AND YYYY-MM-DD
B) Use the WHERE_PARTITIONTIME pseudo column
C) Use SELECT IF.(date >= YYYY-MM-DD AND date <= YYYY-MM-DD
D) Use the TABLE_DATE_RANGE function

3. 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

4. You have a data stored in BigQuery. The data in the BigQuery dataset must be highly available. You need to define a storage, backup, and recovery strategy of this data that minimizes cost. How should you configure the BigQuery table?

A) Create a scheduled query to make copies of the data to tables suffixed with the time of the backup
B) In the event of an emergency, use a point-in-time snapshot to recover the data.
C) Set the BigQuery dataset to be multi-regional
D) Set the BigQuery dataset to be multi-regional
E) In the event of an emergency, use the backup copy of the table.
F) Set the BigQuery dataset to be regiona
G) In the event of an emergency, use a point-in-time snapshot to recover the data.
H) In the event of an emergency, use the backup copy of the table.
I) Set the BigQuery dataset to be regiona
J) Create a scheduled query to make copies of the data to tables suffixed with the time of the backu

5. You are planning to migrate your current on-premises Apache Hadoop deployment to the cloud. You need to ensure that the deployment is as fault-tolerant and cost-effective as possible for long-running batch jobs. You want to use a managed service. What should you do?

A) Install Hadoop and Spark on a 10-node Compute Engine instance group with preemptible instances.Store data in HDF
B) Change references in scripts from hdfs:// to gs://
C) Install Hadoop and Spark on a 10-node Compute Engine instance group with standard instance
D) Store data in Cloud Storage, and change references in scripts from hdfs:// to gs://
E) Use a standard persistent disk and 50% preemptible worker
F) Change references in scripts from hdfs:// to gs://
G) Use an SSD persistent disk and 50% preemptible worker
H) Install the Cloud Storage connector, and store the data in Cloud Storag
I) Deploy a Cloud Dataproc cluste
J) Store data in Cloud Storage, and change references in scripts from hdfs:// to gs://
K) Deploy a Cloud Dataproc cluste