Explanation: Many applications, including those built on modern serverless architectures, can have a large number of open connections to the database server and may open and close database connections at a high rate, exhausting database memory and compute resources. Amazon RDS Proxy allows applications to pool and share connections established with the database, improving database efficiency and application scalability.

Explanation: This solution will meet the requirements because Amazon FSx for NetApp ONTAP is a fully managed service that provides highly reliable, scalable, and feature-rich file storage built on NetApp’s popular ONTAP file system. FSx for ONTAP supports both NFS and SMB protocols, which means it can be accessed by both Linux and Windows applications without code changes. FSx for ONTAP also eliminates data duplication and inefficient resource usage by automatically tiering infrequently accessed data to a lowercost storage tier and providing storage efficiency features such as deduplication and compression. FSx for ONTAP also integrates with other AWS services such as Amazon S3, AWS Backup, and AWS CloudFormation. By migrating the applications to Amazon EC2 instances, the company can leverage the scalability, security, and performance of AWS compute resources.

Explanation: This solution meets the following requirements: It is cost-effective, as it only charges for the storage and data transfer of the replicated objects, and does not require any additional AWS services or custom scripts. S3 Same-Region Replication (SRR) is a feature that automatically replicates objects across S3 buckets within the same AWS Region. SRR can help you aggregate logs from multiple sources to a single destination for analysis and auditing. SRR also preserves the metadata, encryption, and access control of the source objects.

It is operationally efficient, as it does not require any manual intervention or scheduling. SRR replicates objects as soon as they are uploaded to the source bucket, ensuring that the destination bucket always has the latest log data. SRR also handles any updates or deletions of the source objects, keeping the destination bucket in sync. SRR can be enabled with a few clicks in the S3 console or with a simple API call.

It is secure, as it does not allow the logs to leave the us-west-2 Region. SRR only replicates objects within the same AWS Region, ensuring that the data sovereignty and compliance requirements are met. SRR also supports encryption of the source and destination objects, using either server-side encryption with AWS KMS or S3- managed keys, or client-side encryption.

Explanation: This option will configure the development environment in the most cost- effective way as it reduces the number of instances running in the development environment and therefore reduces the cost of running the application. The development environment typically requires less resources than the production environment, and it is unlikely that the development environment will have periods of high traffic that would require a large number of instances. By reducing the maximum number of instances in the development environment's Auto Scaling group, the company can save on costs while still maintaining a functional development environment.

Explanation: This answer is correct because it meets the requirements of transitioning to an event-driven architecture, using serverless concepts, and minimizing operational overhead. AWS Step Functions is a serverless service that lets you coordinate multiple AWS services into workflows using state machines. State machines are composed of tasks and transitions that define the logic and order of execution of the workflow steps. AWS Lambda is a serverless function-as-a-service platform that lets you run code without provisioning or managing servers. Lambda functions can be invoked by Step Functions as tasks in a state machine, and can perform different aspects of the data management workflow, such as data ingestion, transformation, validation, and analysis. By using Step Functions and Lambda, the company can benefit from the following advantages: Event-driven: Step Functions can trigger Lambda functions based on events, such as timers, API calls, or other AWS service events. Lambda functions can also emit events to other services or state machines, creating an event-driven architecture. Serverless: Step Functions and Lambda are fully managed by AWS, so the company does not need to provision or manage any servers or infrastructure. The company only pays for the resources consumed by the workflows and functions, and can scale up or down automatically based on demand. Operational overhead: Step Functions and Lambda simplify the development and deployment of workflows and functions, as they provide built-in features such as monitoring, logging, tracing, error handling, retry logic, and security. The company can focus on the business logic and data processing rather than the operational details.

Explanation: This option is the most efficient because it uses Amazon SQS, which is a fully managed message queuing service that lets you send, store, and receive messages between software components at any volume, without losing messages or requiring other services to be available1. It also uses an SQS message queue to asynchronously dispatch requests to the different application tiers, which decouples the image upload process from the thumbnail generation process and enables scalability and reliability. It also alerts the user through an application message that the image was received, which provides a faster response time to the user than waiting for the thumbnail generation to complete. Option A is less efficient because it uses a custom AWS Lambda function to generate the thumbnail and alert the user, which is a way to run code without provisioning or managing servers. However, this does not use an asynchronous dispatch mechanism to separate the image upload process from the thumbnail generation process. It also uses the image upload process as an event source to invoke the Lambda function, which could cause concurrency issues if there are many images uploaded at once. Option B is less efficient because it uses AWS Step Functions, which is a fully managed service that provides a graphical console to arrange and visualize the components of your application as a series of steps2. However, this does not use an asynchronous dispatch mechanism to separate the image upload process from the thumbnail generation process. It also uses Step Functions to handle the orchestration between the application tiers and alert the user when thumbnail generation is complete, which could introduce additional complexity and latency. Option D is less efficient because it uses Amazon SNS, which is a fully managed messaging service that enables you to send messages or notifications directly to users with SMS text messages or email3. However, this does not use an asynchronous dispatch mechanism to separate the image upload process from the thumbnail generation process. It also uses SNS notification topics and subscriptions to generate the thumbnail after the image upload is complete and message the user’s mobile app by way of a push notification after thumbnail generation is complete, which could introduce additional complexity and latency.

Explanation: To meet the requirements of the web application in the most secure manner, the company should create a Route 53 Resolver outbound endpoint, create a resolver rule, and associate the resolver rule with the VPC. This solution will allow the application to use private DNS records to communicate with the on-premises services from a VPC. Route 53 Resolver is a service that enables DNS resolution between on-premises networks and AWS VPCs. An outbound endpoint is a set of IP addresses that Resolver uses to forward DNS queries from a VPC to resolvers on an on-premises network. A resolver rule is a rule that specifies the domain names for which Resolver forwards DNS queries to the IP addresses that you specify in the rule. By creating an outbound endpoint and a resolver rule, and associating them with the VPC, the company can securely resolve DNS queries for the on-premises services using private DNS records12. The other options are not correct because they do not meet the requirements or are not secure. Creating a Route 53 Resolver inbound endpoint, creating a resolver rule, and associating the resolver rule with the VPC is not correct because this solution will allow DNS queries from on-premises networks to access resources in a VPC, not vice versa. An inbound endpoint is a set of IP addresses that Resolver uses to receive DNS queries from resolvers on an on-premises network1. Creating a Route 53 private hosted zone and associating it with the VPC is not correct because this solution will only allow DNS resolution for resources within the VPC or other VPCs that are associated with the same hosted zone. A private hosted zone is a container for DNS records that are only accessible from one or more VPCs3. Creating a Route 53 public hosted zone and creating a record for each service to allow service communication is not correct because this solution will expose the on-premises services to the public internet, which is not secure. A public hosted zone is a container for DNS records that are accessible from anywhere on the internet3.

Explanation: Deletion protection is a feature of DynamoDB that prevents accidental deletion of tables. When deletion protection is enabled, you cannot delete a table unless you explicitly disable it first. This adds an extra layer of security and reduces the risk of data loss and operational disruption. Deletion protection is easy to enable and disable using the AWS Management Console, the AWS CLI, or the DynamoDB API. This solution has the least operational overhead, as you do not need to create, manage, or invoke any additional resources or services.

Explanation: This option is the most efficient because it deploys the web tier and the application tier to a second Region, which provides high availability and redundancy for the application. It also uses an Amazon Aurora global database, which is a feature that allows a single Aurora database to span multiple AWS Regions1. It also deploys the database in the primary Region and the second Region, which provides low latency global reads and fast recovery from a Regional outage. It also uses Amazon Route 53 health checks with a failover routing policy to the second Region, which provides data protection by routing traffic to healthy endpoints in different Regions2. It also promotes the secondary to primary as needed, which provides data consistency by allowing write operations in one of the Regions at a time3. This solution meets the requirement of expanding globally and ensuring that its application has minimal downtime. Option A is less efficient because it extends the Auto Scaling groups for the web tier and the application tier to deploy instances in Availability Zones in a second Region, which could incur higher costs and complexity than deploying them separately. It also uses an Aurora global database to deploy the database in the primary Region and the second Region, which is correct. However, it does not use Amazon Route 53 health checks with a failover routing policy to the second Region, which could result in traffic being routed to unhealthy endpoints. Option B is less efficient because it deploys the web tier and the application tier to a second Region, which is correct. It also adds an Aurora PostgreSQL cross-Region Aurora Replica in the second Region, which provides read scalability across Regions. However, it does not use an Aurora global database, which provides faster replication and recovery than cross-Region replicas. It also uses Amazon Route 53 health checks with a failover routing policy to the second Region, which is correct. However, it does not promote the secondary to primary as needed, which could result in data inconsistency or loss. Option C is less efficient because it deploys the web tier and the application tier to a second Region, which is correct. It also creates an Aurora PostgreSQL database in the second Region, which provides data redundancy across Regions. However, it does not use an Aurora global database or cross-Region replicas, which provide faster replication and recovery than creating separate databases. It also uses AWS Database Migration Service (AWS DMS) to replicate the primary database to the second Region, which provides data migration between different sources and targets. However, it does not use an Aurora global database or cross-Region replicas, which provide faster replication and recovery than using AWS DMS. It also uses Amazon Route 53 health checks with a failover routing policy to the second Region, which is correct.

Explanation: This solution meets the requirements because it allows the company to improve visibility into the infrastructure by using ALB access logging and Amazon Athena. ALB access logging is a feature that captures detailed information about requests sent to the load balancer, such as the client’s IP address, request path, response code, and latency. By enabling ALB access logging to Amazon S3, the company can store the access logs in an S3 bucket as compressed files. Amazon Athena is an interactive query service that makes it easy to analyze data in Amazon S3 using standard SQL. By creating a table in Amazon Athena for the access logs, the company can query the logs and get results in seconds. This way, the company can better understand the abnormal traffic access patterns across the application.