Back to Amazon Redshift

Amazon Redshift Performance Tuning

5 minutes 5 Questions

Performance tuning in Amazon Redshift involves adjusting your cluster's configuration, table design, and query execution to optimize query performance and ensure maximum efficiency. Key performance tuning concepts include selecting the right node type, optimizing table compression and data distribu…

Test mode:
Start practice test
AWS Certified Solutions Architect - Amazon Redshift Performance Tuning Example Questions

Test your knowledge of Amazon Redshift Performance Tuning

Question 1

A development team runs multiple Amazon Redshift queries on a daily basis for data analysis. Some queries return quickly, while others take several hours. What Amazon Redshift feature can be used to assign the correct concurrency and resource allocation for their workload?

Correct Answer: Workload Management (WLM)

Workload Management (WLM) allows the user to configure different query queues, concurrency and resource allocation depending on the workload. Automatic Table Optimization, Result cache, COPY command, Load error table and Column encoding do not directly address the problem of managing workload priorities.

Question 2

An analytics team loads millions of rows daily into an Amazon Redshift cluster. During business hours, hundreds of concurrent, short-running dashboard queries experience high queue wait times because a few long-running ETL queries run at the same time. The team wants to improve response times for the short queries during peak hours without changing the cluster size or data ingestion schedule. What should they do?

Correct Answer: Implement short query acceleration

Short Query Acceleration (SQA) is designed to prioritize and route eligible short-running queries to a dedicated queue, allowing them to bypass longer-running queries that occupy WLM slots. In the described peak-hour scenario with many short dashboard queries contending with a few long ETL queries, enabling SQA dramatically reduces queue wait time and improves response times without changing cluster size or data ingestion. Increasing node count can help overall capacity but is not the most targeted or cost-effective fix for short-query starvation; reloading data does not address query queuing; and switching to manual resize mode does not improve query performance.

Question 3

A user is experiencing poor query performance on an Amazon Redshift cluster. Upon investigation, it is found that the cluster is not fully utilizing its resources. Which of these steps would help improve the performance of the Amazon Redshift cluster?

Correct Answer: Analyze the WLM configuration and adjust it for optimal resource allocation

Analyzing the workload management (WLM) configuration and adjusting it properly will allow the cluster to allocate its resources more effectively and improve overall performance. Disabling result cache, decreasing node count, increasing column encoding size, enabling Enhanced VPC Routing, and converting to a single-node cluster would not improve the performance given the situation.

More Amazon Redshift Performance Tuning questions
15 questions (total)
Start 15 question test