MCQs on Advanced Query Optimization in Redshift | AWS Redshift Query Tuning Questions

If you’re preparing for AWS certifications or brushing up on Redshift’s advanced query optimization concepts, this set of 30 meticulously designed multiple-choice questions is perfect for you. Covering essential topics like analyzing query plans, table design for performance, materialized views, and advanced joins, these MCQs will help you test your knowledge and gain confidence in Redshift.

Multiple-Choice Questions

1. Analyzing Query Plans (EXPLAIN and SVV Queries)

1. What does the EXPLAIN command in Redshift provide?
   a) Query execution time
   b) Query plan details
   c) SQL syntax errors
   d) Query optimization recommendations
2. Which system view shows details about query performance metrics in Redshift?
   a) SVL_QUERY_METRICS
   b) SVV_QUERY_METRICS
   c) SVL_QUERY_PLANS
   d) SVV_QUERY_EVENTS
3. When analyzing a query plan, which term indicates data redistribution across nodes?
   a) Filter
   b) Broadcast
   c) Hash Join
   d) Sort
4. The EXPLAIN output includes which of the following components?
   a) Execution time in seconds
   b) Query result preview
   c) Logical steps of execution
   d) Query input validation
5. How does Redshift’s EXPLAIN differ from PostgreSQL’s EXPLAIN?
   a) Redshift focuses on distributed systems
   b) Redshift outputs graphical plans
   c) Redshift only shows runtime data
   d) Both are identical

2. Table Design for Performance

6. What is the primary purpose of distribution keys in Redshift?
   a) Optimize joins
   b) Minimize storage costs
   c) Improve sorting
   d) Enhance user permissions
7. Which of the following is NOT a distribution style in Redshift?
   a) EVEN
   b) KEY
   c) ALL
   d) RANDOM
8. How can you optimize table design for a frequent join operation?
   a) Use the same distribution key for both tables
   b) Use the ALL distribution style
   c) Partition tables by date
   d) Use a compound sort key
9. Compound sort keys are most effective when:
   a) Queries use a single column filter
   b) Queries filter data on multiple leading key columns
   c) Tables have no primary key
   d) Distribution style is ALL
10. What is the best practice for large fact tables in Redshift?
    a) Use RANDOM distribution
    b) Use ALL distribution
    c) Use EVEN distribution
    d) Use KEY distribution

3. Materialized Views

11. Materialized views in Redshift are designed to:
    a) Store pre-computed query results
    b) Provide real-time data updates
    c) Replace temporary tables
    d) Encrypt sensitive data
12. Which command is used to refresh a materialized view in Redshift?
    a) UPDATE VIEW
    b) REFRESH MATERIALIZED VIEW
    c) ALTER MATERIALIZED VIEW
    d) SELECT MATERIALIZED VIEW
13. What happens when a materialized view is refreshed?
    a) The original query is re-executed
    b) Data is appended to the view
    c) The view structure is altered
    d) Metadata is updated only
14. Which of the following is a limitation of materialized views in Redshift?
    a) Cannot be queried directly
    b) Cannot include JOINs
    c) Must be refreshed manually
    d) Only support SELECT queries
15. Materialized views are ideal for:
    a) High-throughput OLTP operations
    b) Simplifying complex aggregation queries
    c) Storing user sessions
    d) Real-time analytics

4. Advanced Joins and Subqueries

16. Which type of join redistributes data across nodes to perform the operation?
    a) INNER JOIN
    b) MERGE JOIN
    c) HASH JOIN
    d) CROSS JOIN
17. A correlated subquery:
    a) Executes independently of the outer query
    b) Uses results from the outer query in its execution
    c) Returns only scalar values
    d) Is used exclusively in JOINs
18. What is a key advantage of using CTEs (Common Table Expressions) over subqueries?
    a) CTEs execute faster
    b) CTEs improve query readability
    c) CTEs use less memory
    d) CTEs are automatically materialized
19. Redshift can optimize advanced joins using:
    a) Distribution keys
    b) Compression encoding
    c) Query hints
    d) JSON functions
20. In a multi-table join scenario, performance can be improved by:
    a) Using CROSS JOIN instead of INNER JOIN
    b) Aligning distribution styles
    c) Adding more columns to sort keys
    d) Using random distribution
21. Nested subqueries in Redshift are:
    a) Automatically parallelized
    b) Always optimized for speed
    c) Executed sequentially
    d) Limited to one level
22. Which join type can result in a Cartesian product if no ON condition is specified?
    a) INNER JOIN
    b) FULL OUTER JOIN
    c) CROSS JOIN
    d) LEFT JOIN
23. What is the primary reason to avoid CROSS JOINs?
    a) They are not supported in Redshift
    b) They result in excessive data movement
    c) They require manual optimization
    d) They create large result sets
24. Advanced joins like MERGE JOINs are most effective when:
    a) Data is unsorted
    b) Both tables share the same sort key
    c) Data is randomly distributed
    d) Subqueries are used
25. A lateral join is equivalent to:
    a) CROSS JOIN
    b) INNER JOIN with a subquery
    c) FULL OUTER JOIN
    d) SELF JOIN

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