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An online bookstore uses Amazon Aurora MySQL as its backend database. After the online bookstore added a popular book to the online catalog, customers began reporting intermittent timeouts on the checkout page. A database specialist determined that increased load was causing locking contention on the database. The database specialist wants to automatically detect and diagnose database performance issues and to resolve bottlenecks faster. Which solution will meet these requirements?
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Question 287 - DBS-C01 discussion

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An online bookstore uses Amazon Aurora MySQL as its backend database. After the online bookstore added a popular book to the online catalog, customers began reporting intermittent timeouts on the checkout page. A database specialist determined that increased load was causing locking contention on the database. The database specialist wants to automatically detect and diagnose database performance issues and to resolve bottlenecks faster.

Which solution will meet these requirements?

A.
Turn on Performance Insights for the Aurora MySQL database. Configure and turn on Amazon DevOps Guru for RDS.
Answers
A.
Turn on Performance Insights for the Aurora MySQL database. Configure and turn on Amazon DevOps Guru for RDS.
B.
Create a CPU usage alarm. Select the CPU utilization metric for the DB instance. Create an Amazon Simple Notification Service (Amazon SNS) topic to notify the database specialist when CPU utilization is over 75%.
Answers
B.
Create a CPU usage alarm. Select the CPU utilization metric for the DB instance. Create an Amazon Simple Notification Service (Amazon SNS) topic to notify the database specialist when CPU utilization is over 75%.
C.
Use the Amazon RDS query editor to get the process ID of the query that is causing the database to lock. Run a command to end the process.
Answers
C.
Use the Amazon RDS query editor to get the process ID of the query that is causing the database to lock. Run a command to end the process.
D.
Use the SELECT INTO OUTFILE S3 statement to query data from the database. Save the data directly to an Amazon S3 bucket. Use Amazon Athena to analyze the files for long-running queries.
Answers
D.
Use the SELECT INTO OUTFILE S3 statement to query data from the database. Save the data directly to an Amazon S3 bucket. Use Amazon Athena to analyze the files for long-running queries.
Suggested answer: A

Explanation:

Explanation from Amazon documents:Performance Insights is a feature of Amazon Aurora MySQL that helps you quickly assess the load on your database and determine when and where to take action. Performance Insights displays a dashboard that shows the database load in terms of average active sessions (AAS), which is the average number of sessions that are actively running SQL statements at any given time. Performance Insights also shows the top SQL statements, waits, hosts, and users that are contributing to the database load.Amazon DevOps Guru is a fully managed service that helps you improve the operational performance and availability of your applications by detecting operational issues and recommending specific actions for remediation. Amazon DevOps Guru applies machine learning to automatically analyze data such as application metrics, logs, events, and traces for behaviors that deviate from normal operating patterns. Amazon DevOps Guru supports Amazon RDS as a resource type and can monitor the performance and availability of your RDS databases.By turning on Performance Insights for the Aurora MySQL database and configuring and turning on Amazon DevOps Guru for RDS, the database specialist can automatically detect and diagnose database performance issues and resolve bottlenecks faster. This solution will allow the database specialist to monitor the database load and identify the root causes of performance problems using Performance Insights, and receive actionable insights and recommendations from Amazon DevOps Guru to improve the operational performance and availability of the database.Therefore, option A is the correct solution to meet the requirements. Option B is not sufficient because creating a CPU usage alarm will only notify the database specialist when the CPU utilization is high, but it will not help diagnose or resolve the database performance issues. Option C is not efficient because using the Amazon RDS query editor to get the process ID of the query that is causing the database to lock and running a command to end the process will require manual intervention and may cause data loss or inconsistency. Option D is not efficient because using the SELECT INTO OUTFILE S3 statement to query data from the database and saving the data directly to an Amazon S3 bucket will incur additional time and cost, and using Amazon Athena to analyze the files for long-running queries will not help prevent or resolve locking contention on the database.

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asked 16/09/2024
Mogens Jensen
33 questions
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