Free Practice Questions for Amazon Web Services SAA-C03 Exam

AWS Transfer Family is a secure transfer service that enables you to transfer files into and out of AWS storage services using SFTP, FTPS, FTP, and AS2 protocols. You can use AWS Transfer Family to create an SFTP-enabled server with a public endpoint that allows only trusted IP addresses. You can also attach an Amazon S3 bucket with default encryption enabled to the SFTP service endpoint, which will provide high IOPS performance and highly configurable security for your data at rest. You can also maintain control over user permissions by granting users access to the SFTP service using IAM roles or service-managed identities. References: https://docs.aws.amazon.com/transfer/latest/userguide/what-is-aws-transfer-family.htmlhttps://docs.aws.amazon.com/transfer/latest/userguide/create-server-s3.html

This solution meets the requirements of implementing a solution so that the COTS application can use the data that the legacy application produces with the least operational overhead. AWS Glue is a fully managed service that provides a serverless ETL platform to prepare and load data for analytics. AWS Glue can process data in various formats, including .csv files, and store the processed data in Amazon Redshift, which is a fully managed data warehouse service that supports complex SQL queries. AWS Glue can run ETL jobs on a schedule, which can automate the data processing and loading process.

Option B is incorrect because developing a Python script that runs on Amazon EC2 instances to convert the .csv files to sql files can increase the operational overhead and complexity, and it may not provide consistent data processing and loading for the COTS application. Option C is incorrect because creating an AWS Lambda function and an Amazon DynamoDB table to process the .csv files and store the processed data in the DynamoDB table does not meet the requirement of using Amazon Redshift as the data source for the COTS application. Option D is incorrect because using Amazon EventBridge (Amazon CloudWatch Events) to launch an Amazon EMR cluster on a weekly schedule to process the .csv files and store the processed data in an Amazon Redshift table can increase the operational overhead and complexity, and it may not provide timely data processing and loading for the COTS application.

RDS supported Storage > https://docs.aws.amazon.com/AmazonRDS/latest/UserGuide/CHAP_Storage.html GP2 max IOPS >https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/general-purpose.html#gp2-performance

Amazon RDS provides three storage types: General Purpose SSD (also known as gp2 and gp3), Provisioned IOPS SSD (also known as io1), and magnetic (also known as standard).They differ in performance characteristics and price, which means that you can tailor your storage performance and cost to the needs of your database workload. You can create MySQL,MariaDB, Oracle, and PostgreSQL RDS DB instances with up to 64 tebibytes (TiB) of storage. You can create SQL Server RDS DB instances with up to 16 TiB of storage. For this amount of storage, use the Provisioned IOPS SSD and General Purpose SSD storage types.https://docs.aws.amazon.com/AmazonRDS/latest/UserGuide/CHAP_Storage.html

https://aws.amazon.com/efs/features/infrequent-access/

Auto scaling storage RDS will ease storage issues and migrating Oracle Pl/Sql to Aurora is cumbersome. Also Aurora has auto storage scaling by default.https://docs.aws.amazon.com/AmazonRDS/latest/UserGuide/USER_PIOPS.StorageTypes.html#USER_PIOPS.Autoscaling

" online across multiple AWS Regions at all times". Currently only Read Replica supports cross-regions , Multi-AZ does not support cross-region (it works only in same region)https://aws.amazon.com/about-aws/whats-new/2018/01/amazon-rds-read-replicas-now-support-multi-az-deployments/

Create an Amazon S3 bucket to store the raw data Create an Amazon FSx for Lustre file system that uses persistent SSD storage Select the option to import data from and export data to Amazon S3 Mount the file system on the EC2 instances. Amazon FSx for Lustre uses SSD storage for sub-millisecond latencies and up to 6 GBps throughput, and can import data from and export data to Amazon S3. Additionally, the option to select persistent SSD storage will ensure that the data is stored on the disk and not lost if the file system is stopped.

A Network Load Balancer is a type of load balancer that operates at the connection level (Layer 4) and can load balance both TCP and UDP traffic1. A Network Load Balancer is suitable for scenarios where high performance and low latency are required, such as real-time multiplayer games1. A Network Load Balancer can also handle sudden and volatile traffic patterns while using a single static IP address per Availability Zone1.

To meet the requirements of the scenario, the solutions architect should use a Network Load Balancer for traffic distribution between the EC2 instances in the Auto Scaling group. The Network Load Balancer can route UDPtraffic from the client to the servers onthe appropriate port2. TheNetwork Load Balancer can also support TLS offloading for secure communications between the client and servers1.

Amazon DynamoDB is a fully managed NoSQL database service that can store and retrieve any amount of data with consistent performance and low latency3. Amazon DynamoDB on-demand is a flexible billing option that requires no capacity planning and charges only for the read and write requests that are performed on the tables3. Amazon DynamoDB on-demand is ideal forscenarios where the application traffic is unpredictable or sporadic, such as gaming applications3.

To meet the requirements of the scenario, the solutions architect should use Amazon DynamoDB on-demand for data storage. Amazon DynamoDB on-demand can store gamer scores and other non-relational data without intervention from the developers. Amazon DynamoDB on-demand can also scale automatically to handle any level of requesttraffic without affecting performance or availability3.

Session Manager is a fully managed AWS Systems Manager capability. With Session Manager, you can manage your Amazon Elastic Compute Cloud (Amazon EC2) instances, edge devices, on-premises servers, and virtual machines (VMs). You can use either an interactive one-click browser-based shell or the AWS Command Line Interface (AWS CLI). Session Manager provides secure and auditable node management without the need to open inbound ports, maintainbastion hosts, or manage SSH keys. Session Manager also allows you to comply with corporate policies that require controlled access to managed nodes, strict security practices, and fully auditable logs with node access details, while providing end users with simple one-click cross-platform access to your managed nodes.https://docs.aws.amazon.com/systems-manager/latest/userguide/session-manager.html

By purchasing a Compute Savings Plan, the company can save on the costs of running both EC2 instances and Lambda functions. The Lambda functions can be connected to the private subnet that contains the EC2 instances through a VPC endpoint for AWS services or a VPC peering connection. This provides direct network access to the EC2 instances while keeping the traffic within the private network, which helps to minimize network latency. Optimizing the Lambda functions’ duration, memory usage, number of invocations, and amount of data transferred can help to further minimize costs and improve performance. Additionally, using a private subnet helps to ensure that the EC2 instances are not directly accessible from the public internet, which is a security best practice.

This option is the most efficient because it uses Amazon CloudFront, which is aweb servicethat speeds up distribution of your static and dynamic web content, such as .html, .css, .js, and image files, to your users1. It also uses CloudFront to handle all the requests to the S3 bucket, which reduces the S3 costs by caching the content at the edge locations and serving it from there. It also allows authorized external users to access the documents in milliseconds, as CloudFront delivers the content with low latency and high data transfer rates. This solution meets the requirement of continuing paying to store the data but saving on its total S3 costs. OptionA is less efficient because it configures the S3 bucket to be a Requester Pays bucket, which is a way to shift the cost of data transfer and requests from the bucketowner to the requester2. However, this does not reduce the total S3 costs, as the company still has to pay for storing the data and for any requests made by its own users. Option B is less efficient because it changes the storage tier to S3 Standard for all existing andfuture objects, which is a way to store frequently accessed data withhighdurability and availability3. However, this does not reduce the total S3 costs, as S3 Standard has higher storage costs than S3 Standard-IA. Option C is less efficient because it turns on S3 Transfer Acceleration for the S3 bucket, which is a way to speed uptransfers into and out of an S3 bucket by routing requests through CloudFront edge locations4. However, this does not reduce the total S3 costs, as S3 Transfer Acceleration has additional charges for data transfer and requests.

The allowed block size is between a /28 netmask and /16 netmask. The CIDR block must not overlap with any existing CIDR block that's associated with the VPC.https://docs.aws.amazon.com/vpc/latest/userguide/configure-your-vpc.html

• Amazon S3 for large-scale, durable, and inexpensive storage of the video content. S3 storage costs are significantly lower than EFS. • Amazon EBS only temporarily during processing. By mounting an EBS volume only when a video needs to be processed, and unmounting it after, the time the content spends on the higher-cost EBS storage is minimized. • The EBS volume can be sized to match the workload needs for active processing, keeping costs lower. The volume does not need to store the entire video library long-term.

The best solution to meet these requirements with the least amount of operational overhead is to enable Amazon DynamoDB Streams on the table and use triggers to write to a single AmazonSimple Notification Service (Amazon SNS) topic to which the teams can subscribe. This solution requires minimal configuration and infrastructure setup, and Amazon DynamoDB Streams provide a low-latency way to capture changes to the DynamoDB table. The triggers automatically capture the changes and publish them to the SNS topic, which notifies the internal teams.

https://docs.aws.amazon.com/kinesisanalytics/latest/dev/what-is.html

The combination of steps that will accomplish the task of making the web server accessible from everywhere on port 443 is to create a security group with a rule to allow TCP port 443 from source 0.0.0.0/0 (A) and to update the network ACL to allow inbound TCP port 443 from source 0.0.0.0/0 (C). This will ensure that traffic to port 443 is allowed both at the security group level and at the network ACL level, which will make the web server accessible from everywhere on port 443.

To resolve the issue of slow page loads for a rapidly growing e-commerce website hosted on AWS, a solutions architect can take the following two actions:

1. Set up an Amazon CloudFront distribution

2. Create a read replica for the RDS DB instance

Configuring an Amazon Redshift cluster is not relevant to this issue since Redshift is a data warehousing service and is typically used for the analytical processing of large amounts of data.

Hosting the dynamic web content in Amazon S3 may not necessarily improve performance since S3 is an object storage service, not a web application server. While S3 can be used to host static web content, it may not be suitable for hosting dynamic web content since S3 doesn't support server-side scripting or processing.

Configuring a Multi-AZ deployment for the RDS DB instance will improve high availability but may not necessarily improve performance.

https://aws.amazon.com/blogs/security/rotate-amazon-rds-database-credentials-automatically-with-aws-secrets-manager/

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, whichis 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.

Frequent feature testing -

- Multiple Elastic Beanstalk environments can be created easily for development, testing and production use cases.

- Traffic can be routed between environments for A/B testing and feature iteration using simple URL swapping techniques. No complex routing rules or infrastructure changes required.

Enforce column-level authorization with Amazon QuickSight and AWS Lake Formationhttps://aws.amazon.com/blogs/big-data/enforce-column-level-authorization-with-amazon-quicksight-and-aws-lake-formation/

https://docs.aws.amazon.com/amazondynamodb/latest/developerguide/vpc-endpoints-dynamodb.html

A VPC endpoint for DynamoDB enables Amazon EC2 instances in your VPC to use their private IP addresses to access DynamoDB with no exposure to the public internet. Your EC2 instances do not require public IP addresses, and you don't need an internet gateway, a NAT device, or a virtual private gateway in your VPC. You use endpoint policies to control access to DynamoDB. Traffic between your VPC and the AWS service does not leave the Amazon network.

Amazon DocumentDB (with MongoDB compatibility) is a fast, reliable, and fully managed database service. Amazon DocumentDB makes it easy to set up, operate, and scale MongoDB-compatible databases in the cloud. With Amazon DocumentDB, you can run the same application code and use the same drivers and tools that you use with MongoDB.

https://docs.aws.amazon.com/documentdb/latest/developerguide/what-is.html

https://aws.amazon.com/vi/caching/session-management/

In order to address scalability and to provide a shared data storage for sessions that can be accessible from any individual web server, you can abstract the HTTP sessions from the web servers themselves. A common solution to for this is to leverage an In-Memory Key/Value store such as Redis and Memcached. ElastiCache offerings for In-Memory key/value stores includeElastiCache for Redis, which can support replication, and ElastiCache for Memcached which does not support replication.

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 afailover 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 primaryas needed, which provides dataconsistency 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 secondRegion, 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.

A) Enable AWS CloudTrail and use it for auditing. AWS CloudTrail provides a record of API calls and can be used to audit changes made to EC2 instances and security groups. By analyzing CloudTrail logs, the solutions architect can track who provisioned oversized instances ormodified security groups without proper approval. D) Enable AWS Config and create rules for auditing and compliance purposes. AWS Config can record the configuration changes made to resources like EC2 instances and security groups. The solutions architect can create AWS Config rules to monitor for non-compliant changes, like launching certain instance types or opening security group ports without permission. AWS Config would alert on any violations of these rules.

https://docs.aws.amazon.com/cli/latest/reference/transfer/describe-server.html

- First 30 days- data access every morning ( predictable and frequently) – S3 standard - After 30 days, accessed 4 times a year – S3 infrequently access - Data preserved- S3 Gllacier Deep Archive

https://docs.aws.amazon.com/whitepapers/latest/disaster-recovery-workloads-on-aws/disaster-recovery-options-in-the-cloud.html

This option is the most operationally efficient because it uses AWS Transfer Family to create an FTP server that can store incoming files in Amazon S3 Standard12, which is a low-cost and highly available storage service. It also uses AWS Lambda to process the files and delete them after they are processed, which is a serverless and scalable solution that does not require any batch scheduling or infrastructure management. It also uses S3 event notifications to invoke the Lambda function when the files arrive, which enables near real-time processing of the incoming data files3. Option A is less efficient because it uses Amazon S3 Glacier Flexible Retrieval, which is a cold storage class that has higher retrieval costs and longer retrieval times than Amazon S3 Standard. It also uses EventBridge rules to invoke the job nightly, which does notmeet the requirement of processing incoming data files as soon as possible. Option B is less efficient because it uses an EBS volume to store incoming files, which is a block storage service that has higher costs and lower durability than Amazon S3. It also uses EventBridge rules to invoke the job nightly, which does not meet the requirement of processing incoming data files as soon as possible. Option C is less efficient because it uses an EBS volume to store incoming files, which is a block storage service that has higher costs and lower durability than Amazon S3. It also uses AWS Batch to process the files, which requires managing compute resources and job queues.

Amazon Aurora Serverless for MySQL is a fully managed, auto-scaling relational database service that scales up or down automatically based on the application demand. This service provides all the capabilities of Amazon Aurora, such as high availability, durability, and security, without requiring the customer to provision any database instances. With Amazon Aurora Serverless for MySQL, the sales team can enjoy minimal downtime since the database is designed to automatically scale to accommodate the increased traffic. Additionally, the service allows the customer to pay only for the capacity used, making it cost-effective for infrequent access patterns. Amazon RDS for MySQL could also be an option, but it requires the customer to select an instance type, and the database administrator would need to monitor and adjust the instance size manually to accommodate the increasing traffic.

Read replica use cases - You have a production database that is taking on normal load & You want to run a reporting application to run some analytics • You create a Read Replica to run the new workload there • The production application is unaffected • Read replicas are used for SELECT (=read) only kind of statements (not INSERT, UPDATE, DELETE)

S3 One Zone-IA is a storage class that is designed for data that is accessed less frequently, but requires rapid access when needed. Unlike other S3 Storage Classes which store data in a minimum of three Availability Zones (AZs), S3 One Zone-IA stores data in a single AZ and costs 20% less than S3 Standard-IA. This storage class meets the requirements of the company because it provides a low-cost solution for the secondary copy of its on-premises dataset that would rarely need to be accessed. The other storage classes are either more expensive or not suitable for infrequently accessed data.

https://docs.aws.amazon.com/AmazonS3/latest/userguide/storage-class-intro.html

 Security groups are virtual firewalls that protect AWS instances and can be applied to EC2, ELB and RDS1. Security groups have rules for inbound and outbound traffic and are stateful, meaning that responses to allowed inbound traffic are allowed to flow out of the instance2. Network ACLs are different from security groups in several ways. They cover entire subnets, not individual instances, and are stateless, meaning that they require rules for both inbound and outbound traffic2. Network ACLs also support deny rules, while security groups only support allow rules2.

To meet the requirements of the scenario, the solutions architect should create two security groups: one for the DB instance and one for the web servers in the public subnet. The security group for the DB instance should allow traffic from the public subnet CIDR block on port 3306, which is the default port for MySQL3. This way, only the web servers in the public subnet can access the DB instance on that port. The security group for the web servers should allow traffic from 0 0 0 O’O on port 443, which is the default port forHTTPS4. This way, the web servers can accept secure connections from the internet on that port.

This answer is correct because it provides a resilient and durable replacement for the on-premises file share that is compatible with Windows IIS web servers. Amazon FSx for Windows File Server is a fully managed service that provides shared file storage built on Windows Server. It supports the SMB protocol and integrates with Microsoft ActiveDirectory, which enables seamless access and authentication for Windows-based applications. Amazon FSx for Windows File Server also offers the following benefits:

Resilience: Amazon FSx for Windows File Server can be deployed in multiple Availability Zones, which provides high availability and failover protection. It also supports automatic backups and restores, as well as self-healing features that detect and correct issues.

Durability: Amazon FSx for Windows File Server replicates data within and across Availability Zones, and stores data on highly durable storage devices. It also supports encryption at rest and in transit, as well as file access auditing and data deduplication.

Performance: Amazon FSx for Windows File Server delivers consistent sub-millisecond latencies and high throughput for file operations. It also supports SSD storage, native Windows features such as Distributed File System (DFS) Namespaces and Replication, and user-driven performance scaling.

By configuring the Amazon FSx file share to use an AWS KMS CMK to encrypt the images in the file share, the company can protect the images from unauthorized access and comply with company policy. By using NTFS permission sets on the images, the company can prevent accidental deletion of the images by restricting who can modify or delete them.

The most operationally efficient solution to meet these requirements would be to create a backup plan by using AWS Backup to perform nightly backups and copying the backups to another Region. Adding the application's EBS volumes as resources will ensure that the application's EC2 instance configuration and data are backed up, and copying the backups to another Region will ensure that the application is recoverable in a different AWS Region.

The data retention period of a Kinesis data stream is the time period from when a record is added to when it is no longer accessible1. Thedefault retention period for a Kinesis data stream is 24 hours, which can be extended up to 8760 hours (365 days)1. The dataretention period can be updated by using the AWS Management Console, the AWS CLI, or the Kinesis Data Streams API1.

To meet the requirements of the scenario, the solutions architect should update the Kinesis Data Streams default settings by modifying the data retention period. The solutions architect should increase the retention period to a value that is greater than or equal to thefrequency of consuming the data and writing itto S32. This way, the company can ensure that S3 receives all the data that the application sends to Kinesis Data Streams.

This option is the mostefficient because it sets an overall password policy for the entire AWS account, which is a way to specify complexity requirements and mandatory rotationperiods for IAM user passwords1. It also meets the requirement of setting a password policy for all new users, as the password policy applies to all IAM users in the account. This solution meets the requirement of setting specific complexity requirements and mandatory rotation periods for IAM user passwords. Option B is less efficient because it sets a password policy for each IAM user in the AWS account, which is not possible as password policies can only be set at the account level. Option C is less efficient because it uses third-party vendor software to set password requirements, which is not necessary as IAM provides a built-in way to set password policies. Option D is less efficient because it attaches an Amazon CloudWatch rule to the Create_newuser event to set the password with the appropriate requirements, which is not possible as CloudWatch rules cannot modify IAM user passwords.

https://docs.aws.amazon.com/sns/latest/dg/sns-server-side-encryption.htmlhttps://docs.aws.amazon.com/AWSSimpleQueueService/latest/SQSDeveloperGuide/sqs-server-side-encryption.html

The most efficient solution for automatically starting and stopping EC2 instances and DB instances on a schedule while minimizing cost and infrastructure maintenance is to create an AWS Lambda function and configure Amazon EventBridge to invoke the function on a schedule.

Option A, scaling EC2 instances by using elastic resize and scaling DB instances to zero outside of business hours, is not feasible as DB instances cannot be scaled to zero.

Option B, exploring AWS Marketplace for partner solutions, may be an option, but it may not be the most efficient solution and could potentially add additional costs.

Option C, launching another EC2 instance and configuring a crontab schedule to run shell scripts that will start and stop the existing EC2 instances and DB instances on a schedule, adds unnecessary infrastructure and maintenance.

• Launching instances within a single AZ and using a cluster placement group provides the lowest network latency and highest bandwidth between instances. This maximizes performance for an in-memory database and high-throughput application.

• Communications between instances in the same AZ and placement group are free, minimizing data transfer charges. Inter-AZ and public IP traffic can incur charges.

• A cluster placement group enables the instances to be placed close together within the AZ, allowing the high network throughput required. Partition groups span AZs, reducing bandwidth.

• Auto Scaling across zones could launch instances in AZs that increase data transfer charges. It may reduce network throughput, impacting performance.

"RESTful web services" => API Gateway.

"EC2 instance receives the raw data, transforms the raw data, and stores all the data in an Amazon S3 bucket" => GLUE with (Extract - Transform - Load)

https://medium.com/awesome-cloud/aws-difference-between-multi-az-and-read-replicas-in-amazon-rds-60fe848ef53a

Amazon EFS provides a simple, scalable, fully managed file system that can be simultaneously accessed from multiple EC2 instances and provides built-in redundancy. It is optimized for multiple EC2 instances to access the same files, and it is designed to be highly available, durable, and secure. It can scale up to petabytes of data and can handle thousands of concurrent connections, and is a cost-effective solution for storing and accessing large amounts of data.

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.

AWS Global Accelerator: This service provides a global fixed entry point to your applications and optimizes the path to your application through the AWS global network, reducing latency and improving performance.

Accelerated TCP Connections:

Global Accelerator uses the AWS global network to route traffic to the nearest edge location, improving the performance and reliability of your live streams.

It provides static IP addresses that act as a fixed entry point to your application, simplifying DNS management.

High-Quality Streams:

By leveraging Global Accelerator, reporters can send live streams with the highest quality and low latency.

This service automatically reroutes traffic to the nearest available AWS Region, ensuring consistent performance even during traffic spikes or failures.

Operational Efficiency: Using Global Accelerator simplifies the network setup and provides an optimized path for live streams without the need for complex configurations, making it an efficient solution for real-time streaming applications.

AWS CloudTrail Lakeis a fully managed service that allows the collection, storage, and querying ofCloudTrail eventsfor both AWS and non-AWS services. CloudTrail Lake can be customized to collect logs from various sources, ensuring a centralized audit solution. It also supports long-term storage, so logs can be retained for 7 years, meeting the compliance requirement.

Option A (Data Lake): Setting up a data lake in S3 introduces unnecessary operational complexity compared to CloudTrail Lake.

Option C (Ingest non-AWS services into CloudTrail): CloudTrail Lake is better suited for this task with less operational overhead.

Option D (CloudWatch Logs): While CloudWatch can store logs, CloudTrail Lake is specifically designed for API auditing and storage.

AWS References:

AWS CloudTrail Lake

For the migration of data from an on-premises Oracle database to Amazon Aurora PostgreSQL, this solution effectively handles schema conversion, data migration, and ongoing data replication.

AWS Schema Conversion Tool (SCT): SCT is used to convert the Oracle database schema to a format compatible with Aurora PostgreSQL. This tool automatically converts the database schema and code objects, like stored procedures, to the target database engine.

AWS Database Migration Service (DMS): DMS is employed to perform the data migration. It supports both full-load migrations (for initial data transfer) and continuous replication of ongoing changes (Change Data Capture, or CDC). This ensures that any updates to the Oracle database during the migration are captured and applied to the Aurora PostgreSQL database, minimizing downtime.

Why Not Other Options?:

Option A (SCT + DMS full-load only): This option does not capture ongoing changes, which is crucial for a live database migration to ensure data consistency.

Option B (DataSync + S3): AWS DataSync is more suited for file transfers rather than database migrations, and it doesn’t support ongoing change replication.

Option D (Snowball + S3): Snowball is typically used for large-scale data transfers that don’t require continuous synchronization, making it less suitable for this scenario where ongoing changes must be captured.

AWS References:

AWS Schema Conversion Tool- Guidance on using SCT for database schema conversions.

AWS Database Migration Service- Detailed documentation on using DMS for data migrations and ongoing replication.

Amazon FSx for Lustre: Lustre is a high-performance file system designed for workloads that require fast storage with sustained high throughput and low latency. It integrates with Amazon S3, making it suitable for HPC environments.

Persistent File Systems:

Persistent Storage: Suitable for long-term storage and recurrent use, providing durability and availability.

High Throughput and Low Latency: Persistent Lustre file systems can handle large amounts of data with sub-millisecond latency, meeting the needs of high-performance computing workloads.

Simultaneous Access: FSx for Lustre allows thousands of compute instances to access and process large datasets concurrently, ensuring that the high volume of data is handled efficiently.

Highly Available: FSx for Lustre is designed to provide high availability and is managed by AWS, reducing the operational burden.

Amazon FSx for Lustre is the ideal solution for high-performance computing (HPC) workloads that require parallel access to a shared file system with low latency. FSx for Lustre is designed specifically to meet the needs of such workloads, offering sub-millisecond latencies, which makes it well-suited for the 1 ms latency requirement mentioned in the question.

Here is why FSx for Lustre is the best fit:

Parallel File System: FSx for Lustre is a parallel file system that can scale across hundreds of Amazon EC2 instances, providing high throughput and low-latency access to data. It is optimized for processing large datasets in parallel, which is essential for HPC workloads.

Low Latency: FSx for Lustre is capable of providing access latencies well within 1 ms, making it ideal for performance-sensitive workloads like HPC.

Seamless Integration with Amazon S3: FSx for Lustre can be linked to an Amazon S3 bucket. This integration allows data to be imported from S3 into FSx for Lustre before the workload begins and exported back to S3 after processing. This feature is crucial for manual postprocessing because it enables engineers to access the dataset in S3 after processing.

Performance: FSx for Lustre is built for workloads that require high performance, such as machine learning, analytics, media processing, and financial simulations, which are typical for HPC environments.

In contrast:

Amazon EFS (Option A): While EFS provides shared file storage and scales across multiple EC2 instances, it does not offer the same level of performance or sub-millisecond latencies as FSx for Lustre. EFS is more suited for general-purpose workloads, not high-performance computing.

Mounting S3 as a file system (Option B and D): S3 is object storage, not a file system designed for low-latency access and parallel processing. Mounting S3 buckets directly or using AWS Resource Access Manager to share the bucket would not meet the low-latency (1 ms) or performance requirements needed for HPC workloads.

Therefore, Amazon FSx for Lustre (Option C) is the most appropriate and verified solution for this scenario.

AWS References:

Amazon FSx for Lustre

Best Practices for High Performance Computing (HPC)

Amazon FSx and Amazon S3 Integration