SAA-C03 · Question #357
A company stores a large volume of critical data in Amazon RDS for PostgreSQL tables. The company is developing several new features for an upcoming product launch. Some of the new features require…
The correct answer is D. Use an Amazon RDS blue/green deployment to deploy a new test instance of the database. Amazon RDS Blue/Green Deployments provide a safe and straightforward way to make database changes with minimal downtime and risk. Blue/Green deployments create an exact copy ("green") of your production environment ("blue") where you can make schema changes and run tests. After…
Question
A company stores a large volume of critical data in Amazon RDS for PostgreSQL tables. The company is developing several new features for an upcoming product launch. Some of the new features require many table alterations. The company needs a solution to test the altered tables for several days. After testing, the solution must make the new features available to customers in production. Which solution will meet these requirements with the HIGHEST availability?
Options
- ACreate a new instance of the database in RDS for PostgreSQL to test the new features. When the
- BCreate new database tables in the production database to test the new features. When the testing
- CCreate an Amazon RDS read replica to deploy a new instance of the database. Make updates to
- DUse an Amazon RDS blue/green deployment to deploy a new test instance of the database.
How the community answered
(39 responses)- A8% (3)
- B3% (1)
- C18% (7)
- D72% (28)
Explanation
Amazon RDS Blue/Green Deployments provide a safe and straightforward way to make database changes with minimal downtime and risk. Blue/Green deployments create an exact copy ("green") of your production environment ("blue") where you can make schema changes and run tests. After validation, you can promote the green environment to production with a single click or API call, achieving near-zero downtime and maximum availability. This is the AWS-recommended method for deploying major database changes in a way that minimizes impact to users and maximizes uptime.
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