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Google Data Engineer Sample Questions:

1. Which of these sources can you not load data into BigQuery from?

A) Google Drive
B) Google Cloud Storage
C) File upload
D) Google Cloud SQL

2. Each analytics team in your organization is running BigQuery jobs in their own projects. You want to enable each team to monitor slot usage within their projects. What should you do?

A) Create a Stackdriver Monitoring dashboard based on the BigQuery metric query/scanned_bytes
B) Create a log export for each project, capture the BigQuery job execution logs, create a custom metric based on the totalSlotMs, and create a Stackdriver Monitoring dashboard based on the custom metric
C) Create a Stackdriver Monitoring dashboard based on the BigQuery metric slots/allocated_for_project
D) Create an aggregated log export at the organization level, capture the BigQuery job execution logs, create a custom metric based on the totalSlotMs, and create a Stackdriver Monitoring dashboard based on the custom metric

3. You work for an economic consulting firm that helps companies identify economic trends as they happen. As part of your analysis, you use Google BigQuery to correlate customer data with the average prices of the 100 most common goods sold, including bread, gasoline, milk, and others. The average prices of these goods are updated every 30 minutes. You want to make sure this data stays up to date so you can combine it with other data in BigQuery as cheaply as possible. What should you do?

A) Store the data in Google Cloud Datastor
B) Use Google Cloud Dataflow to query BigQuery and combine the data programmatically with the data stored in Cloud Datastore
C) Load the data every 30 minutes into a new partitioned table in BigQuery.
D) Store the data in a file in a regional Google Cloud Storage bucke
E) Store and update the data in a regional Google Cloud Storage bucket and create a federated data source in BigQuery
F) Use Cloud Dataflow to query BigQuery and combine the data programmatically with the data stored in Google Cloud Storage.

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

5. Which of these rules apply when you add preemptible workers to a Dataproc cluster (select 2 answers)?

A) A Dataproc cluster cannot have only preemptible workers.
B) Preemptible workers cannot store data.
C) Preemptible workers cannot use persistent disk.
D) If a preemptible worker is reclaimed, then a replacement worker must be added manually.

Solutions: