Google Cloud SQL - Integrating Cloud SQL with Google Cloud Storage MCQ

Google Cloud SQL – 38 – Integrating Cloud SQL with Google Cloud Storage MCQ

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1. What is the primary benefit of integrating Google Cloud SQL with Google Cloud Storage?

Real-time data analysis

Improved query optimization

Enhanced database performance

Cost-effective data storage

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2. Which database engines are supported for integration with Google Cloud Storage through Google Cloud SQL?

MySQL and PostgreSQL

SQL Server and Cassandra

MySQL and Oracle

MongoDB and Redis

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3. How can Google Cloud Storage be leveraged for data backup in the Cloud SQL integration?

To store database backups and snapshots

To execute SQL scripts

To store database configuration settings

To run database queries

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4. Which tool can be used to export data from a Cloud SQL instance to Cloud Storage for backup?

Google BigQuery

gcloud command-line tool

psql command

mysqldump command

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5. What is the primary advantage of scheduling automatic database backups to Cloud Storage?

Facilitating disaster recovery

Ensuring data consistency

Enabling real-time data analysis

Reducing storage costs

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6. What is IAM, and why is it important when integrating Cloud SQL with Cloud Storage?

Integrated Access Management, used for data storage optimization

Internet Authentication Method, essential for data transfer

Identity and Access Management, used to control access to resources

Integrated Authorization Model, essential for query optimization

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7. What should be implemented to protect data during transfer and storage in the Cloud SQL and Cloud Storage integration?

Strong access controls, encryption, and IAM policies

Real-time data analysis

Frequent data compression

Data visualization tools

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8. How can organizations optimize Cloud Storage costs in the integration?

Setting up retention policies and lifecycle rules for objects

Enabling real-time data analysis

Implementing strong access controls

Frequent data backups

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9. What is the recommended approach to perform data import/export between Cloud SQL and Cloud Storage?

Using data visualization tools

Using mysqldump or pg_dump for export and mysql or psql for import

Leveraging SQL queries

Manually copying files

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10. What is the primary focus of data encryption when integrating Cloud SQL with Cloud Storage?

Enabling real-time data analysis

Automating data compression

Enhancing query optimization

Protecting data during transfer and storage

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11. What Google Cloud service can be used alongside the Cloud SQL and Cloud Storage integration for data analysis and processing?

Google Dataflow

Google Cloud Pub/Sub

Google Cloud Functions

Google BigQuery

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12. Why is monitoring and logging important in the context of the integration?

To optimize query performance

To track data movement and assess performance

To reduce data transfer time

To automate data compression

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13. What is the primary purpose of data sharing between Cloud SQL and Cloud Storage in the integration?

To improve data transfer efficiency

To perform real-time data analysis

To create frequent data backups

To facilitate data analysis and processing

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14. What role does IAM play in controlling access to resources in the integration?

Identity and Access Management

Integrated Access Management

Internet Authorization Model

Integrated Authorization Model

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15. Why is setting up appropriate access controls and permissions important in the integration?

To reduce data transfer costs

To ensure data security and compliance

To improve data visualization

To automate data backup

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16. Which practice is NOT recommended for successful integration of Cloud SQL with Cloud Storage?

Frequent data backups

Implementing strong access controls

Ignoring data encryption during transfer

Regularly monitoring and optimizing performance

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17. What is the primary role of Cloud Storage in the integration when it comes to database backups?

Real-time data analysis

Running database queries

Storing database backups and snapshots

Data visualization

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18. How can organizations ensure data protection and disaster recovery in the integration?

By avoiding data import/export

By storing database backups in Cloud Storage

By disabling IAM

By implementing strong access controls

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19. What is the recommended approach for minimizing data transfer costs between Cloud SQL and Cloud Storage?

Implementing cost-effective storage practices

Frequent data compression

Data visualization tools

Setting up retention policies for all objects

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20. What practice should organizations follow to optimize data backup and recovery capabilities in the integration?

Disabling IAM for all users

Implementing real-time data analysis

Frequent data import/export

Scheduling automatic database backups

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21. Which Google Cloud service can be used for data analysis and processing alongside the integration?

Google Bigtable

Google Cloud Dataprep

Google Cloud Datastore

Google BigQuery

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22. What is the primary goal of optimizing storage costs in the integration?

Minimizing data transfer time

Implementing strong access controls

Cost-effective storage of large volumes of data

Reducing data encryption

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23. How can organizations ensure that Cloud SQL and Cloud Storage are effectively integrated for data import/export?

By implementing strong access controls

By following best practices and scheduled backups

By avoiding data compression

By using data visualization tools

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24. Why is continuous monitoring of the integration’s performance essential?

To improve query optimization

To reduce data transfer costs

To automate data encryption

To track data movement and assess performance

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25. What is the primary purpose of real-time data analysis in the integration?

To perform frequent data backups

To enable cost-effective storage practices

To enhance data visualization

To facilitate data analysis and processing

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Google Cloud SQL – Integrating Cloud SQL with Google Cloud Storage MCQ