01. Your IT team uses BigQuery for storing structured data. Your finance team recently moved to Google Workspace Enterprise edition from a standalone, desktop-based spreadsheet processor. When the finance team needs data insights, the IT team runs a query on BigQuery, exports the data to a CSV file, and sends the file as an email attachment to the finance team members.
You want to improve the process while you retain familiar methods of data analysis for the finance team.
What should you do?
a) Run the query in BigQuery, and give the finance team access to the results view, which can be analyzed.
b) Run the query in BigQuery, and save the results to a Google Sheets shared spreadsheet that can be accessed and analyzed by the finance team.
c) Run the query in BigQuery, and give the finance team access to the data visualizations in Looker Studio.
d) Run the query in BigQuery, export the data to CSV, upload the file to a Cloud Storage bucket, and share the file with the finance team.
02. The Dataflow SDKs were donated to the Apache Software Foundation and became which Apache project?
a) Apache Beam
b) Apache Spark
c) Apache Hadoop
d) Apache Kafka
03. You are migrating an application that tracks library books and information about each book, such as author or year published, from an on-premises data warehouse to BigQuery. In your current relational database, the author information is kept in a separate table and joined to the book information on a common key.
Following Google's recommended practice for schema design, how would you structure the data to ensure optimal speed of queries about the author of each book that has been borrowed?
a) Keep the schema the same, maintain the different tables for the book and each of the attributes, and query as you are doing today
b) Create a table that is wide and includes a column for each attribute, including the author's first name, last name, date of birth, etc
c) Create a table that includes information about the books and authors, but nest the author fields inside the author column
d) Keep the schema the same, create a view that joins all of the tables, and always query the view
04. What is the general recommendation when designing your row keys for a Bigtable schema?
a) Concatenate several time-series measurement values into the row key
b) Make your row key as long as Bigtable permits, so that more context is stored in it
c) Store the row key as a fixed-width 64-bit integer that increments with each write
d) Keep your row key reasonably short, using only the fields you query by
05. Your company runs its business-critical system on PostgreSQL. The system is accessed simultaneously from many locations around the world and supports millions of customers, and your database administration team manages the redundancy and scaling manually.
You want to migrate the database to Google Cloud. You need a solution that will provide global scale and availability and require minimal maintenance.
What should you do?
a) Migrate to BigQuery.
b) Migrate to a Cloud SQL for PostgreSQL instance.
c) Migrate to Spanner.
d) Migrate to bare metal machines with PostgreSQL installed.
06. For the best possible performance, what is the recommended zone placement for your Compute Engine instance relative to your Bigtable instance?
a) Have the Compute Engine instance and the Bigtable instance in the same zone.
b) Have the Compute Engine instance and the Bigtable instance in different zones.
c) Have the Compute Engine instance in the furthest zone from the Bigtable instance.
d) Have the Bigtable instance in the same zone as all of the consumers of your data.
07. Which of the following is not one of the three main types of triggers that Dataflow supports?
a) Trigger that is a combination of other triggers
b) Trigger based on element size in bytes
c) Trigger based on element count
d) Trigger based on time
08. All Bigtable client requests go through a front-end server ______ they are sent to a Bigtable node.
a) before
b) after
c) only if
d) once
09. Which of the following statements is NOT true regarding Bigtable access roles?
a) Bigtable IAM policies can be granted at the project, instance, or table level.
b) Granting a Bigtable role at the instance level gives the principal that access on every table in the instance.
c) You can configure access control only at the project level.
d) Roles granted at the project level apply to every Bigtable instance in that project.
10. When running a pipeline that has a BigQuery source on your local machine, you continue to get permission denied errors.
What could be the reason for that?
a) Pipelines with a BigQuery source cannot be run with the local runner.
b) BigQuery datasets cannot be read from a machine outside Google Cloud.
c) The gcloud CLI is missing from your local machine, so no request is sent.
d) Your local gcloud credentials do not have access to the BigQuery resources.