Google Cloud SQL - Query optimization in Cloud SQL MCQ

Google Cloud SQL – 40 – Query optimization in Cloud SQL MCQ

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1. What is the primary goal of query optimization in Google Cloud SQL?

Enhancing data visualization

Improving query efficiency and execution time

Increasing data encryption overhead

Maximizing data transfer costs

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2. Why is query optimization crucial for database performance in Google Cloud SQL?

To improve response times and resource efficiency

To increase data encryption overhead

To minimize storage costs

To reduce query efficiency

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3. What is one of the benefits of optimized queries in Google Cloud SQL?

Decreased application response times

Reduced CPU and memory resource consumption

Lower user satisfaction

Slower query execution

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4. What can happen if indexes are not used efficiently in Google Cloud SQL queries?

Improved query performance

Slower query execution

Reduced storage overhead

Decreased resource efficiency

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5. Which tool can be used to monitor query performance in Google Cloud SQL?

Google Dataflow

Google Cloud Pub/Sub

Google Cloud Dataprep

Query Insights feature in Google Cloud Console

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6. How can organizations identify slow queries and areas for optimization in Google Cloud SQL?

By increasing CPU and memory resources

By using the EXPLAIN statement

By implementing automatic scaling

By running periodic load tests

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7. What is the primary purpose of normalizing data in the context of query optimization in Google Cloud SQL?

Increasing data encryption overhead

Reducing data redundancy and improving query performance

Minimizing data transfer costs

Enhancing data visualization

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8. Which SQL clause is used to refine query results in Google Cloud SQL?

GROUP BY

SELECT *

WHERE

JOIN

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9. What should be avoided when optimizing queries in Google Cloud SQL?

Reviewing query execution plans

Enabling query caching for dynamic data

Using subqueries instead of JOINs

Selecting only the necessary columns

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10. Why should organizations review and modify indexes when optimizing queries in Google Cloud SQL?

To enhance data visualization

To ensure they align with query patterns and avoid unnecessary overhead

To maximize storage overhead

To reduce query execution time

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11. What is the recommended approach for optimizing queries that use subqueries in Google Cloud SQL?

Use subqueries whenever possible to simplify query structure

Disable query caching for subquery results

Rewrite subqueries as JOINs for better performance

Increase the number of indexes on relevant columns

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12. What can be achieved by enabling query caching in Google Cloud SQL?

Slower query execution times

Reduced resource efficiency

Faster query execution for frequently used queries

Increased data encryption overhead

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13. When should organizations consider upgrading CPU and memory resources for their Google Cloud SQL database instance?

When query caching is disabled

When reducing resource utilization is a priority

When query optimization doesn't yield desired results

When using subqueries in queries

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14. What is the primary benefit of benchmarking queries in Google Cloud SQL?

To increase CPU and memory resource consumption

To improve query efficiency

To identify performance bottlenecks and measure the impact of query changes

To decrease data transfer costs

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15. What is the primary role of the EXPLAIN statement in Google Cloud SQL query optimization?

To analyze how the database executes a query and identify potential bottlenecks

To create indexes

To execute queries and retrieve results

To enhance data visualization

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16. Which SQL clause can be used to refine query results to retrieve only the necessary data in Google Cloud SQL?

WHERE

GROUP BY

SELECT *

JOIN

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17. Why should organizations avoid excessive indexing when optimizing queries in Google Cloud SQL?

To reduce data redundancy

To minimize CPU resource consumption

To maximize query efficiency

To increase storage overhead

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18. What type of JOIN is recommended for better performance in Google Cloud SQL queries?

LEFT JOIN

INNER JOIN

FULL JOIN

SELF JOIN

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19. What can happen if wildcard characters like “%” are used at the beginning of a search pattern in Google Cloud SQL queries?

Increased CPU utilization

Slower query execution

Reduced resource consumption

Improved query performance

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20. How can organizations optimize query performance for specific use cases in Google Cloud SQL?

By increasing the number of columns in SELECT *

By always normalizing data

By denormalizing data when necessary

By using subqueries extensively

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21. Which tool can help identify potential performance bottlenecks in Google Cloud SQL queries?

Google Cloud Pub/Sub

Google Dataflow

Google Cloud Dataprep

Cloud SQL Query Insights

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22. What is the primary purpose of avoiding cursor usage in Google Cloud SQL queries?

To increase data encryption overhead

To enhance data visualization

To prevent excessive database calls and improve query performance

To simplify query structure

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23. What role does efficient application code play in query optimization in Google Cloud SQL?

To increase CPU and memory resource consumption

To generate inefficient queries

To maximize data transfer costs

To optimize SQL statements generated by the application

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24. When should organizations consider upgrading hardware resources for their Google Cloud SQL database instance?

When query performance is not satisfactory after other optimizations

When query optimization is successful

When using wildcard characters in queries

When the database schema is being modified

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25. What should be the primary goal when optimizing database schema in Google Cloud SQL?

Enhancing data visualization

Maximizing storage overhead

Increasing data encryption overhead

Reducing data redundancy and improving query performance

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Google Cloud SQL – Query optimization in Cloud SQL MCQ