SAA-C03 Exam Dumps - AWS Certified Solutions Architect - Associate (SAA-C03)

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.

The solution that will meet the requirements is to create Amazon Kinesis data streams for data ingestion, create Amazon Kinesis Data Firehose delivery streams to consume the Kinesis data streams, and specify the S3 bucket as the destination of the delivery streams. This solution will allow the company’s application to ingest real-time data from third-party applications and place the ingested raw data in an S3 bucket. Amazon Kinesis data streams are scalable and durable streams that can capture and store data from hundreds of thousands of sources. Amazon Kinesis Data Firehose is a fully managed service that can deliver streaming data to destinations such as S3, Amazon Redshift, Amazon OpenSearch Service, and Splunk. Amazon Kinesis Data Firehose can also transform and compress the data before delivering it to S3.

The other solutions are not as effective as the first one because they either do not support real-time data ingestion, do not work with third-party applications, or do not use S3 as the destination. Creating database migration tasks in AWS Database Migration Service (AWS DMS) will not support real-time data ingestion, as AWS DMS is mainly designed for migrating relational databases, not streaming data. AWS DMS also requires replication instances, source endpoints, and target endpoints to be compatible with specific database engines and versions. Creating and configuring AWS DataSync agents on the EC2 instances will not work with third-party applications, as AWS DataSync is a service that transfers data between on-premises storage systems and AWS storage services, not between applications. AWS DataSync also requires installing agents on the source or destination servers. Creating an AWS Direct Connect connection to the application for data ingestion will not use S3 as the destination, as AWS Direct Connect is a service that establishes a dedicated network connection between on-premises andAWS, not between applications and storage services. AWS Direct Connect also requires a physical connection to an AWS Direct Connect location.

This solution meets the following requirements:

It is operationally efficient, as it only requires one transit gateway and one transit VIF to connect the Direct Connect connection to all the VPCs in the same AWS Region. The transit gateway acts as a regional network hub that simplifies the network management and reduces the number of VIFs and gateways needed.

It is scalable, as it can support up to 5000 attachments per transit gateway, which can include VPCs, VPNs, Direct Connect gateways, and peering connections. The transit gateway can also be connected to other transit gateways in different Regions or accounts using peering connections, enabling cross-Region and cross-account connectivity.

It is flexible, as it allows each VPC to communicate with all other VPCs and on-premises networks using dynamic routing protocols such as Border Gateway Protocol (BGP). The transit gateway’s route propagation feature automatically propagates the routes from the attached VPCs and VPNs to the transit gateway route table, eliminating the need to manually update the route tables.

The S3 Block Public Access feature allows you to restrict public access to S3 buckets and objects within the account. You can enable this feature at the account level to prevent any S3 bucket from being made public, regardless of the bucket policy settings. AWS Organizations can be used to apply a Service Control Policy (SCP) to the account to prevent IAM users from changing this setting, ensuring that all S3 objects remain private. This is a straightforward and effective solution that requires minimal operational overhead.

These two steps will meet the requirements because they will provide high availability and scalability for the web application without rewriting it. Amazon EC2 Auto Scaling allows you to automatically adjust the number of EC2 instances in response to changes in demand. By configuring Auto Scaling with multiple Availability Zones in private subnets, you can ensure that your web application is distributed across isolated and fault-tolerant locations, and that your instances are not directly exposed to the internet. An Application Load Balancer operates at the application layer and distributes incoming web traffic across multiple targets, such as EC2 instances, containers, or Lambda functions. By configuring an Application Load Balancer in a public subnet, you can enable your web application to handle requests from the internet and route them to the appropriate targets in the private subnets.

AWS WAF is a web application firewall that helps protect web applications from common web exploits that could affect application availability, compromise security, or consume excessive resources. AWS WAF allows users to create rules that block, allow, or count web requests based on customizable web security rules. One of the types of rules that can be created is an IP match rule, which allows users to specify a list of IP addresses or IP address ranges that they want to allow or block. By modifying the configuration of AWS WAF to add an IP match condition to block the malicious IP address, the solution architect can prevent the attacker from accessing the website through the CloudFront distribution and the ALB.

The other options are not correct because they do not effectively block the malicious IP address from accessing the website. Modifying the network ACL on the CloudFront distribution or the EC2 instances in the target groups behind the ALB will not work because network ACLs are stateless and do not evaluate traffic at the application layer. Modifying the security groups for the EC2 instances in the target groups behind the ALB will not work because security groups are stateful and only evaluate traffic at the instance level, not at the load balancer level.

 This option is the most efficient because it uses Amazon CloudFront, which is a web service that speeds up distribution of your static and dynamic web content, such as .html, .css, .js, and image files, to your users1. It also uses a CloudFront distribution for the static content, which reduces the load on the EC2 instances and improves the performance and availability of the website. It also uses an Auto Scaling group to launchnew instances based on network traffic, which automatically adjusts the compute capacity of your EC2 instances based on load or aschedule2. This solution meets the requirement of ensuring that all the requests are processed successfully during events for the launch of new products. Option A is less efficient because it uses a CloudFront distribution for the dynamic content, which is not necessary as the dynamic content is already handled by the API on the EC2 instances. It also increases the number of EC2 instances to handle the increase in traffic, which could incur higher costs and complexity than using an Auto Scaling group. Option C is less efficient because it uses an Amazon ElastiCache instance in front of the ALB to reduce traffic for the API to handle, which is a way to provide afully managed in-memory data store service that provides sub-millisecond latency for caching and data processing3. However, this could introduce additional complexity and latency, and does not scale automatically based on network traffic. Option D is less efficient because it uses an Amazon Simple Queue Service (Amazon SQS) queue to receive requests from the website for later processing by the EC2 instances, which is a way to send, store, and receive messages between software components at any volume. However, this does not provide a faster response time to the users as they have to wait for their requests to be processed by the EC2 instances.

https://docs.aws.amazon.com/autoscaling/ec2/userguide/ec2-auto-scaling-mixed-instances-groups.html