Explanation: This option is the most cost-effective and scalable way to allow the Lambda function in the primary account to access the EFS file system in the secondary account. VPC peering enables private connectivity between two VPCs without requiring gateways, VPN connections, or dedicated network connections. The Lambda function can use the VPC peering connection to mount the EFS file system as a local file system and access the files as needed. The solution does not incur additional data transfer or storage costs, and it leverages the existing EFS file system without duplicating or moving the data. Option A is not cost-effective because it requires creating a new EFS file system and using AWS DataSync to copy the data from the original EFS file system. This would incur additional storage and data transfer costs, and it would not provide real-time access to the files. Option C is not scalable because it requires creating a second Lambda function in the secondary account and configuring cross-account permissions to invoke it from the primary account. This would add complexity and latency to the solution, and it would increase the Lambda invocation costs. Option D is not feasible because Lambda layers are not designed to store large amounts of data or provide file system access. Lambda layers are used to share common code or libraries across multiple Lambda functions. Moving the contents of the EFS file system to a Lambda layer would exceed the size limit of 250 MB for a layer, and it would not allow the Lambda function to read or write files to the layer.

Explanation: 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 launch new instances based on network traffic, which automatically adjusts the compute capacity of your EC2 instances based on load or a schedule2. 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 a fully 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.

Explanation: This option is the most efficient because it uses AWS Storage Gateway, which is a service that connects an on-premises software appliance with cloud-based storage to provide seamless integration with data security features between your on-premises IT environment and the AWS storage infrastructure1. It also uses a stored volume gateway, which is a type of volume gateway that stores your primary data locally and asynchronously backs up point-in-time snapshots of your data to Amazon S32. It also runs the Storage Gateway software application on premises and maps the gateway storage volumes to on-premises storage, which enables you to use your existing storage hardware and network infrastructure. It also mounts the gateway storage volumes to provide local access to the data, which ensures that your data is available for low latency access on premises while also getting backed up to AWS. This solution meets the requirement of maintaining local access to all the data while it is backed up on AWS and ensuring that the data backed up on AWS is automatically and securely transferred. Option A is less efficient because it uses AWS Snowball, which is a physical device that lets you transfer large amounts of data into and out of AWS3. However, this does not provide a periodic backup solution, as it requires manual handling and shipping of the device. It also configures on-premises systems to mount the Snowball S3 endpoint to provide local access to the data, which could introduce additional complexity and latency. Option B is less efficient because it uses AWS Snowball Edge, which is a physical device that has onboard storage and compute capabilities for select AWS capabilities. However, this does not provide a periodic backup solution, as it requires manual handling and shipping of the device. It also uses the Snowball Edge file interface to provide on-premises systems with local access to the data, which could introduce additional complexity and latency. Option C is less efficient because it uses AWS Storage Gateway and configures a cached volume gateway, which is a type of volume gateway that stores your primary data in Amazon S3 and retains a copy of frequently accessed data subsets locally. However, this does not provide local access to all the data, as only some data subsets are cached locally. It also configures a percentage of data to cache locally, which could incur higher costs and complexity than using a stored volume gateway.

Explanation: This option is the most cost-effective and simple way to enable SFTP access to the S3 data lake. AWS Transfer Family is a fully managed service that supports secure file transfers over SFTP, FTPS, and FTP protocols. You can create an SFTP-enabled server with a public endpoint and associate it with your S3 bucket. You can also use AWS Identity and Access Management (IAM) roles and policies to control access to your S3 data lake. The service scales automatically to handle any volume of file transfers and provides high availability and durability. You do not need to provision, manage, or patch any servers or load balancers.

Option B is not correct because Amazon S3 File Gateway is not an SFTP server. It is a hybrid cloud storage service that provides a local file system interface to S3. You can use it to store and retrieve files as objects in S3 using standard file protocols such as NFS and SMB. However, it does not support SFTP protocol, and it requires deploying a file gateway appliance on-premises or on EC2.

Option C is not cost-effective or scalable because it requires launching and managing an EC2 instance in a private subnet and setting up a VPN connection for the new partner. This would incur additional costs for the EC2 instance, the VPN connection, and the data transfer. It would also introduce complexity and security risks to the solution. Moreover, it would require running a cron job script on the EC2 instance to upload files to the S3 data lake, which is not efficient or reliable.

Option D is not cost-effective or scalable because it requires launching and managing multiple EC2 instances in a private subnet and placing a NLB in front of them. This would incur additional costs for the EC2 instances, the NLB, and the data transfer. It would also introduce complexity and security risks to the solution. Moreover, it would require running a cron job script on the EC2 instances to upload files to the S3 data lake, which is not efficient or reliable.

Explanation: The solution that will improve the performance of the data tier is to deploy an Amazon ElastiCache for Redis cluster in front of the existing DB instance and modify the game to use Redis. This solution will enable the game to store and retrieve the location data of the players in a fast and scalable way, as Redis is an in-memory data store that supports geospatial data types and commands. By using ElastiCache for Redis, the game can reduce the load on the RDS for PostgreSQL DB instance, which is not optimized for high-frequency updates and queries of location data. ElastiCache for Redis also supports replication, sharding, and auto scaling to handle the increasing user base of the game. The other solutions are not as effective as the first one because they either do not improve the performance, do not support geospatial data, or do not leverage caching. Taking a snapshot of the existing DB instance and restoring it with Multi-AZ enabled will not improve the performance of the data tier, as it only provides high availability and durability, but not scalability or low latency. Migrating from Amazon RDS to Amazon OpenSearch Service with OpenSearch Dashboards will not improve the performance of the data tier, as OpenSearch Service is mainly designed for full-text search and analytics, not for real-time location tracking. OpenSearch Service also does not support geospatial data types and commands natively, unlike Redis. Deploying Amazon DynamoDB Accelerator (DAX) in front of the existing DB instance and modifying the game to use DAX will not improve the performance of the data tier, as DAX is only compatible with DynamoDB, not with RDS for PostgreSQL. DAX also does not support geospatial data types and commands.

Explanation: Amazon Transcribe now supports speaker labeling for streaming transcription. Amazon Transcribe is an automatic speech recognition (ASR) service that makes it easy for you to convert speech-to-text. In live audio transcription, each stream of audio may contain multiple speakers. Now you can conveniently turn on the ability to label speakers, thus helping to identify who is saying what in the output transcript.

Explanation: The most suitable design change for the company’s application is to request strongly consistent reads for the table. This change will ensure that the requests to the table return the latest data, reflecting the updates from all prior write operations. Amazon DynamoDB is a fully managed NoSQL database service that provides fast and predictable performance with seamless scalability. DynamoDB supports two types of read consistency: eventually consistent reads and strongly consistent reads. By default, DynamoDB uses eventually consistent reads, unless users specify otherwise1.

Eventually consistent reads are reads that may not reflect the results of a recently completed write operation. The response might not include the changes because of the latency of propagating the data to all replicas. If users repeat their read request after a short time, the response should return the updated data. Eventually consistent reads are suitable for applications that do not require up-to-date data or can tolerate eventual consistency1.

Strongly consistent reads are reads that return a result that reflects all writes that received a successful response prior to the read. Users can request a strongly consistent read by setting the ConsistentRead parameter to true in their read operations, such as GetItem, Query, or Scan. Strongly consistent reads are suitable for applications that require up-todate data or cannot tolerate eventual consistency1.

The other options are not correct because they do not address the issue of read consistency or are not relevant for the use case. Adding read replicas to the table is not correct because this option is not supported by DynamoDB. Read replicas are copies of a primary database instance that can serve read-only traffic and improve availability and performance. Read replicas are available for some relational database services, such as Amazon RDS or Amazon Aurora, but not for DynamoDB2. Using a global secondary index (GSI) is not correct because this option is not related to read consistency. A GSI is an index that has a partition key and an optional sort key that are different from those on the base table. A GSI allows users to query the data in different ways, with eventual consistency3. Requesting eventually consistent reads for the table is not correct because this option is already the default behavior of DynamoDB and does not solve the problem of requests not returning the latest data.

Explanation: As they would like to retrieve the data with sub-millisecond, DynamoDB with DAX is the answer. DynamoDB supports some of the world's largest scale applications by providing consistent, single-digit millisecond response times at any scale. You can build applications with virtually unlimited throughput and storage.