Google Cloud SQL - Advanced indexing strategies MCQ

Google Cloud SQL – 41 – Advanced indexing strategies MCQ

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1. What is the primary purpose of advanced indexing strategies in Google Cloud SQL?

Reducing query execution times and improving database efficiency

Enhancing data visualization

Increasing storage costs

Maximizing CPU utilization

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2. How can advanced indexing benefit query performance in Google Cloud SQL?

By slowing down query execution times

By allowing queries to find and retrieve data more quickly

By minimizing storage costs

By increasing CPU and memory resource consumption

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3. Which type of index includes all the columns needed for a query, eliminating additional lookups?

Expression Index

Partial Index

Composite Index

Covering Index

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4. In Google Cloud SQL, when might composite indexes be particularly effective?

When complex expressions are used in queries

When data filtering is common

When queries don't involve multiple columns

When covering indexes are already in place

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5. What is the primary role of partial indexes in Google Cloud SQL?

To increase index size

To speed up all queries

To reduce query execution times for specific scenarios

To eliminate the need for composite indexes

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6. In PostgreSQL on Google Cloud SQL, what type of index can be used to improve query performance for large datasets when searching for values?

Bloom Filter

Expression Index

Clustered Index

Covering Index

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7. What is the main function of clustered indexes in SQL Server on Google Cloud SQL?

To reduce query efficiency

To minimize CPU utilization

To create composite indexes

To determine the physical order of rows in a table

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8. When analyzing query patterns, what should organizations identify to determine which columns to index?

Columns with the lowest cardinality

Columns used in WHERE clauses, JOIN conditions, and ORDER BY clauses

All columns in the database

Columns used in SELECT statements

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9. What should be included in covering indexes in Google Cloud SQL?

Random columns to increase index size

All columns needed for specific queries

Only a single column used in queries

All columns in the database

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10. When creating composite indexes in Google Cloud SQL, what should be considered?

The most frequently used column in the table

The least frequently used columns

Only single-column indexes

Combinations of columns frequently used together in queries

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11. What can expression indexes in Google Cloud SQL help optimize?

CPU utilization

Query execution times

Storage overhead

Complex expressions in queries

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12. Why is it essential to regularly monitor and tune indexes in Google Cloud SQL?

To increase storage costs

To speed up all queries

To minimize resource efficiency

To adapt to evolving query patterns and data changes

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13. Which type of index can reduce the computational burden of query processing in Google Cloud SQL?

Covering Index

Clustered Index

Bloom Filter

Expression Index

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14. In which database engine on Google Cloud SQL are clustered indexes particularly relevant?

MySQL

PostgreSQL

SQL Server

Oracle

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15. What is the primary goal of utilizing Bloom filters in PostgreSQL on Google Cloud SQL?

Enhancing data visualization

Increasing storage overhead

Improving query performance for value lookups in large datasets

Slowing down query execution

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16. When should organizations consider implementing expression indexes in Google Cloud SQL?

When using subqueries extensively

When upgrading CPU and memory resources

When queries involve complex expressions

When normalizing data

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17. How can organizations optimize query performance for specific scenarios in Google Cloud SQL?

By creating composite indexes

By enabling query caching

By implementing expression indexes

By always using partial indexes

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18. What is the role of advanced indexing strategies in reducing resource consumption in Google Cloud SQL?

To increase CPU and memory resource utilization

To slow down query execution times

To maximize storage costs

To minimize resource consumption by speeding up queries

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19. What is the primary function of clustered indexes in SQL Server on Google Cloud SQL?

To determine the physical order of rows in a table

To create partial indexes

To increase CPU utilization

To reduce query efficiency

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20. Which type of index can significantly improve the performance of JOIN operations in Google Cloud SQL?

Composite Index

Clustered Index

Covering Index

Expression Index

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21. What is the primary benefit of utilizing partial indexes in Google Cloud SQL?

Reducing query efficiency

Optimizing query performance for specific scenarios

Minimizing query execution times

Increasing index size

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22. In PostgreSQL on Google Cloud SQL, what role do Bloom filters play in query optimization?

They enhance data visualization

They slow down query execution

They increase storage overhead

They improve query performance for value lookups in large datasets

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23. What is the purpose of composite indexes in Google Cloud SQL?

To cover combinations of columns frequently used together in queries

To optimize queries involving complex expressions

To eliminate the need for covering indexes

To increase index size

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24. What is the primary goal of covering indexes in Google Cloud SQL?

To include only a single column used in queries

To minimize query execution times by eliminating additional lookups

To slow down query performance

To maximize CPU utilization

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25. How can organizations reduce storage costs while optimizing query performance in Google Cloud SQL?

By implementing advanced indexing strategies

By creating expression indexes for all columns

By using random indexes

By implementing partial indexes for all data

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Google Cloud SQL – Advanced indexing strategies MCQ