SAA-C03 Practice Test

Explanation: For C: IMPROVED USER EXPERIENCE Lambda@Edge can help improve your users' experience with your websites and web applications across the world, by letting you personalize content for them without sacrificing performance. Real-time Image Transformation You can customize your users' experience by transforming images on the fly based on the user characteristics. For example, you can resize images based on the viewer's device type—mobile, desktop, or tablet. You can also cache the transformed images at CloudFront Edge locations to further improve performance when delivering images. https://aws.amazon.com/lambda/edge/

Explanation: To meet the requirements of immediately accessible records for 1 year and then archived for an additional 9 years with maximum resiliency, we can use S3 Lifecycle policy to transition records from S3 Standard to S3 Glacier Deep Archive after 1 year. And to ensure that the records cannot be deleted by anyone, including administrative and root users, we can use S3 Object Lock in compliance mode for a period of 10 years. Therefore, the correct answer is option C.

Explanation: Amazon Aurora is a fully managed relational database that is compatible with MySQL and PostgreSQL. It provides up to five times better performance than MySQL and up to three times better performance than PostgreSQL. It also provides high availability and durability by replicating data across multiple Availability Zones and continuously backing up data to Amazon S31. By using Amazon RDS deployed in a Multi-AZ instance deployment to create an Amazon Aurora database, the solution can achieve higher availability and improve application performance. Amazon Aurora supports read replicas, which are separate instances that share the same underlying storage as the primary instance. Read replicas can be used to offload read-only queries from the primary instance and improve performance. Read replicas can also be used for reporting functions2. By directing the reporting functions to the reader instances, the solution can offload reporting from its primary database system. Use AWS Database Migration Service (AWS DMS) to create an Amazon RDS DB instance in multiple AWS Regions Point the reporting functions toward a separate DB instance from the pri-mary DB instance. This solution will not meet the requirement of using an AWS database service, as AWS DMS is a service that helps users migrate databases to AWS, not a database service itself. It also involves creating multiple DB instances in different Regions, which may increase complexity and cost. Use Amazon RDS in a Single-AZ deployment to create an Oracle database Create a read replica in the same zone as the primary DB instance. Direct the reporting functions to the read replica. This solution will not meet the requirement of achieving higher availability, as a Single-AZ deployment does not provide failover protection in case of an Availability Zone outage. It also involves using Oracle as the database engine, which may not provide better performance than Aurora. Use Amazon RDS deployed in a Multi-AZ cluster deployment to create an Oracle database Di-rect the reporting functions to use the reader instance in the cluster deployment. This solution will not meet the requirement of improving application performance, as Oracle may not provide better performance than Aurora. It also involves using a cluster deployment, which is only supported for Aurora, not for Oracle.

Explanation: Dynamic scaling is a type of autoscaling that automatically adjusts the number of EC2 instances in an Auto Scaling group based on demand or load. It uses CloudWatch alarms to trigger scaling actions when a specified metric crosses a threshold. It can scale out (add instances) or scale in (remove instances) as needed1. By using dynamic scaling, the solution can maintain application performance during sudden traffic increases most cost-effectively. Use manual scaling to change the size of the Auto Scaling group. This solution will not meet the requirement of maintaining application performance during sudden traffic increases, as manual scaling requires users to manually increase or decrease the number of instances through a CLI or console. It does not respond automatically to changes in demand or load2. Use predictive scaling to change the size of the Auto Scaling group. This solution will not meet the requirement of most cost-effectiveness, as predictive scaling uses machine learning and artificial intelligence tools to evaluate traffic loads and anticipate when more or fewer resources are needed. It performs scheduled scaling actions based on the prediction, which may not match the actual demand or load at any given time. Predictive scaling is more suitable for scenarios where there are predictable traffic patterns or known changes in traffic loads3. D. Use schedule scaling to change the size of the Auto Scaling group. This solution will not meet the requirement of maintaining application performance during sudden traffic increases, as schedule scaling performs scaling actions at specific times that users schedule. It does not respond automatically to changes in demand or load. Schedule scaling is more suitable for scenarios where there are predictable traffic drops or spikes at specific times of the day.