Google Cloud SQL - Performance tuning for large datasets MCQ

Google Cloud SQL – 39 – Performance tuning for large datasets MCQ

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1. What is the primary goal of performance tuning for large datasets in Google Cloud SQL?

Optimizing query efficiency and database performance

Enhancing data visualization

Reducing data encryption overhead

Minimizing data transfer costs

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2. What challenges can large datasets in Google Cloud SQL pose in terms of performance?

Data compression and encryption

Frequent backups and slow query execution

Slow data import/export

High resource consumption and low query latency

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3. What can happen if indexing is not properly implemented for large datasets in Cloud SQL?

Improved query performance and reduced query execution time

Frequent data backups and recovery issues

Increased storage overhead and slower write operations

Enhanced data compression and lower latency

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4. Which strategy can be employed to distribute data across multiple tables or databases for improved scalability in Cloud SQL?

Data normalization

Data visualization

Frequent data imports

Table partitioning or sharding

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5. What is the primary purpose of query optimization in the context of large datasets in Google Cloud SQL?

Enhancing data visualization

Minimizing data transfer costs

Optimizing query efficiency and execution time

Reducing data encryption overhead

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6. Why is it important to implement appropriate access controls and permissions for large datasets in Google Cloud SQL?

To ensure data security and compliance

To optimize query performance

To facilitate real-time data analysis

To reduce data encryption overhead

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7. What is the recommended approach for optimizing CPU and memory resources when dealing with large datasets in Cloud SQL?

Monitor resource utilization and adjust as needed

Implement manual scaling

Use query caching extensively

Disable connection pooling

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8. How can organizations efficiently manage and reuse database connections when dealing with large datasets?

Use connection pooling

Implement automatic scaling

Increase the number of indexes

Disable query caching

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9. What can be used to identify slow-performing queries and areas for query optimization in Google Cloud SQL?

Google Cloud Storage

Query Insights feature in Google Cloud Console

Google Cloud Dataflow

Google BigQuery

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10. Which tool can help simulate peak traffic and identify potential performance bottlenecks in Cloud SQL for large datasets?

Google Dataflow

Google Cloud Dataprep

Apache JMeter

Google Cloud Pub/Sub

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11. Why is it crucial to monitor resource utilization in Google Cloud SQL when dealing with large datasets?

To optimize CPU and memory resources

To ensure data security and compliance

To reduce data transfer costs

To enhance data visualization

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12. What is the primary role of query hints when optimizing queries in Cloud SQL?

To enable data compression

To facilitate real-time data analysis

To guide the query optimizer in query execution

To automate data backups

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13. What approach should organizations follow to achieve optimal database performance for large datasets in Cloud SQL?

Increasing data encryption overhead

Continuous monitoring and periodic tuning

Avoiding query optimization

Frequent data imports

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14. How can organizations efficiently manage resources during peak traffic in Cloud SQL for large datasets?

Decrease the number of indexes

Implement automatic scaling based on traffic patterns

Disable resource scaling

Disable connection pooling

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15. What practice should organizations avoid when optimizing Cloud SQL performance for large datasets?

Frequent backups

Implementing appropriate access controls

Proper indexing

Over-indexing

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16. Which factor can lead to high latency in Cloud SQL when handling large datasets?

Inadequate indexing

Data distribution using sharding

Low query latency

Efficient resource utilization

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17. What can happen if resources are not scaled adequately to meet the demands of large datasets in Cloud SQL?

Increased costs and decreased query efficiency

Enhanced data visualization

Improved query performance

Decreased query execution time

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18. What is the primary focus of query caching in Google Cloud SQL when dealing with large datasets?

Enhancing data visualization

Reducing query execution time by reusing query results

Reducing data transfer costs

Implementing strong access controls

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19. How can organizations optimize CPU and memory resources for large datasets in Cloud SQL efficiently?

Monitoring resource utilization and adjusting as needed

Disabling connection pooling

Over-indexing database tables

Implementing manual scaling

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20. What is the primary purpose of data normalization in the context of large datasets in Google Cloud SQL?

Reducing storage overhead and improving query efficiency

Enhancing data visualization

Increasing data encryption overhead

Minimizing data transfer costs

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21. Why is implementing automatic scaling important when dealing with variable traffic patterns in Cloud SQL for large datasets?

To efficiently manage resources during traffic spikes

To facilitate data analysis and processing

To ensure constant resource utilization

To minimize data transfer time

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22. What is the primary role of Google Cloud Dataflow in optimizing performance for large datasets in Cloud SQL?

Increasing data encryption overhead

Implementing query hints

Enhancing data visualization

Data preprocessing and distribution

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23. What is the primary goal of implementing partitioning or sharding when optimizing Cloud SQL performance for large datasets?

Improving data visualization

Distributing data across multiple tables or databases for improved scalability

Avoiding query optimization

Reducing data encryption overhead

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24. Which Google Cloud service can be used for monitoring and alerting key database metrics in Cloud SQL?

Google Cloud Pub/Sub

Google Cloud Dataprep

Google Cloud Monitoring

Google Dataflow

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25. What is the primary purpose of load testing in the context of optimizing Cloud SQL for large datasets?

To enhance data visualization

To reduce data transfer costs

To simulate peak traffic and identify potential bottlenecks

To facilitate real-time data analysis

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Google Cloud SQL – Performance tuning for large datasets MCQ