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Google Data Engineer Sample Questions:

1. You are building a new data pipeline to share data between two different types of applications: jobs generators and job runners. Your solution must scale to accommodate increases in usage and must accommodate the addition of new applications without negatively affecting the performance of existing ones. What should you do?

A) Use a Cloud Pub/Sub topic to publish jobs, and use subscriptions to execute them
B) Create a table on Cloud Spanner, and insert and delete rows with the job information
C) Create a table on Cloud SQL, and insert and delete rows with the job information
D) Create an API using App Engine to receive and send messages to the applications

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

3. You are designing storage for two relational tables that are part of a 10-TB database on Google Cloud. You want to support transactions that scale horizontally. You also want to optimize data for range queries on nonkey columns. What should you do?

A) Use Cloud Spanner for storag
B) Use Cloud SQL for storag
C) Add secondary indexes to support query patterns.
D) Use Cloud SQL for storag
E) Use Cloud Dataflow to transform data to support query patterns.
F) Use Cloud Dataflow to transform data to support query patterns.
G) Add secondary indexes to support query patterns.
H) Use Cloud Spanner for storag

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) Set the BigQuery dataset to be multi-regional
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) Create a scheduled query to make copies of the data to tables suffixed with the time of the backup
E) Create a scheduled query to make copies of the data to tables suffixed with the time of the backu
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) In the event of an emergency, use the backup copy of the table.

5. 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) Only use service accounts when sharing data for Cloud Storage buckets and BigQuery datasets.
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) Use Cloud Deployment Manager to automate access provision.
E) For each Cloud Storage bucket or BigQuery dataset, decide which projects need acces
F) Introduce resource hierarchy to leverage access control policy inheritance.

Solutions: