Incremental Exports: Exporting DynamoDB data directly to Amazon S3 provides an automated, serverless way to make data available for analytics without operational overhead.

Analytics-Friendly Storage: Amazon S3 supports long-term analytics workloads and can integrate with tools like Athena or QuickSight.

DynamoDB Export to S3 Documentation

The most secure option is to use across-account IAM rolein the central logging account and allow authorized Lambda functions in member accounts to assume that role. AWS cross-account access guidance is based on trusted roles rather than sharing long-term IAM user credentials. This keeps access temporary, auditable, and centrally controlled. The IAM user answers are weaker because they depend on long-term credentials, which is not the recommended pattern for Lambda-based cross-account access. A bucket policy that broadly allows “AWS Lambda” is also too permissive and not scoped to the organization’s trusted workloads. Therefore, a central role with an appropriate trust policy is the strongest design among the listed choices.

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Amazon S3 is the strongest storage choice because it is designed for highly durable object storage, and CloudFront can use an S3 bucket as its origin to deliver content globally. AWS Backup documentation also states that for Amazon S3 you can createcontinuous backupsand restore data to a point in time, which reduces the risk of data loss more effectively than simple daily backups. The EC2-based options introduce more infrastructure management and weaker durability characteristics than S3 for this content-storage pattern. Lambda ephemeral storage is temporary and is not intended as a durable content repository. For globally distributed campaign assets that must stay highly available and protected with the strongest managed backup posture among the options,S3 + CloudFront + AWS Backup continuous backupsis the best design. (AWS Documentation)

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Amazon Aurora MySQL–compatible offers a distributed, fault-tolerant storage system with Multi-AZ high availability and supports Aurora Replicas that “share the same underlying storage” for low-latency reads. Aurora provides Aurora Auto Scaling to “automatically add or remove Aurora Replicas based on load,” ideal for unpredictable, read-heavy workloads. This architecture offloads reads from the writer and maintains HA through automatic failover. Amazon RDS Single-AZ (B) lacks HA. Redshift (A) is a data warehouse, not a transactional DB. ElastiCache (D) can reduce read pressure but does not provide durable read replicas or automatic HA scaling at the database tier. Aurora’s design directly addresses the requirement to automatically scale reads while maintaining availability, matching Well-Architected guidance to use managed, elastic services for variable demand.

Amazon S3 is the most cost-effective option for archiving data. Using AWS DMS to migrate to S3 in Apache Parquet format provides an optimized columnar format for analytics. By storing in S3 Glacier Flexible Retrieval, costs are minimized while maintaining compliance with retention. When queries are required, Athena can run SQL queries directly on archived data in S3 without provisioning infrastructure. Options A and B rely on maintaining RDS or EC2 instances, which increases cost and operational overhead. Option C requires full restores to EC2 before running queries, which is slow and inefficient. Therefore, D provides the lowest operational overhead and direct query capability with Athena.

To meet the requirement of deleting objects older than 3 years while retaining certain data, this solution leverages serverless technologies to minimize operational overhead.

S3 Inventory: S3 Inventory provides a flat file that lists all the objects in an S3 bucket and their metadata, which can be configured to include data such as the last modified date. This inventory can be generated daily or weekly.

AWS Lambda Function: A Lambda function can be created to process the S3 Inventory report, filtering out the objects that need to be retained and identifying those that should be deleted.

S3 Batch Operations: S3 Batch Operations can execute tasks such as object deletion at scale. By invoking the Lambda function through S3 Batch Operations, you can automate the process of deleting the identified objects, ensuring that the solution is serverless and requires minimal operational management.

Why Not Other Options?:

Option A (AWS CLI script on EC2): Running a script on an EC2 instance adds unnecessary operational overhead and is not serverless.

Option B (AWS Batch): AWS Batch is designed for running large-scale batch computing workloads, which is overkill for this scenario.

Option C (AWS Glue + script): AWS Glue is more suited for ETL tasks, and this approach would add unnecessary complexity compared to the serverless Lambda solution.

AWS References:

Amazon S3 Inventory- Information on how to set up and use S3 Inventory.

S3 Batch Operations- Documentation on how to perform bulk operations on S3 objects using S3 Batch Operations.

The correct answer isCbecause the company already usesAmazon FSx for NetApp ONTAPand requires a disaster recovery solution in a secondary Region that preserves access through thesame protocols, specificallyCIFS and NFS. The most appropriate solution is to deploy a secondFSx for ONTAPfile system in the secondary Region and useNetApp SnapMirrorto replicate data between Regions. SnapMirror is built for ONTAP-based replication and is the native mechanism for efficient, storage-level disaster recovery.

This option provides the least operational overhead because it uses the capabilities already integrated into FSx for ONTAP rather than introducing another storage platform or custom replication tooling. It also preserves protocol compatibility, so applications in the recovery Region can continue to access data through CIFS and NFS without architectural changes.

Option A is incorrect because replicating data to Amazon S3 does not preserve native CIFS and NFS file shares. Option B can support recovery, but backups and restores are not the best fit for an active DR strategy where the secondary Region needs accessible replicated storage with the same protocols and lower recovery effort. Option D is incorrect because Amazon EFS does not provide CIFS/SMB support in the same way as FSx for ONTAP and would require a platform change.

AWS guidance for FSx for ONTAP disaster recovery favors usingSnapMirror replicationto another FSx for ONTAP deployment. This approach is protocol-compatible, efficient, and operationally simpler than building a custom DR workflow.

Comprehensive and Detailed 250 to 300 words of Explanation (AWS documentation-based, no links):

The requirements are straightforward: expose the Lambda microservice through an HTTPS endpoint and authenticate calls using IAM. Lambda function URLs are a built-in feature that provides a dedicated HTTPS endpoint for a Lambda function without requiring API Gateway, ALB, or CloudFront. When configured with the authentication type AWS_IAM, the endpoint requires requests to be signed with AWS Signature Version 4 and authorized by IAM policies. This directly satisfies the “must use IAM to authenticate calls” requirement with the least architectural complexity.

Option A can also secure an endpoint with IAM, but it proposes using a Lambda authorizer, which is typically used for custom authorizers (JWT/OAuth/Cognito/external identity). For IAM authentication in API Gateway, you generally use IAM authorization on the method, not an authorizer function. Also, API Gateway REST APIs introduce additional service configuration and per-request costs when a simpler managed option exists that meets the requirements.

Options C and D are not appropriate. Lambda@Edge and CloudFront Functions run at CloudFront edge locations with different programming and deployment models; they are designed for CDN request/response manipulation, not as the primary mechanism to expose a regional Lambda microservice endpoint with IAM authentication. CloudFront Functions in particular is for lightweight JavaScript at the edge and does not provide a native “AWS_IAM authentication type” for invoking an origin Lambda as a microservice endpoint.

Therefore, B is the cleanest and most secure fit: a native HTTPS endpoint backed by Lambda, protected with IAM-based SigV4 authentication.

The AWS Cost and Usage Report (CUR) delivers detailed, line-item billing data to Amazon S3. AWS recommends querying CUR with Amazon Athena by creating external tables over the CUR S3 location (partitioned by time) to produce daily cost aggregations such as NAT Gateway (EC2:NatGateway) usage and cost. Amazon QuickSight natively connects to Athena as a data source to build and share dashboards with visuals (tables, time series) filtered from the start of the current month. DataSync (A, C) is a file transfer service and cannot query data. CloudWatch dashboards (C, D) visualize metrics/logs, not CUR datasets. Therefore, using Athena to query CUR and QuickSight to present a daily NAT gateway cost dashboard is the most direct and operationally efficient approach.

The problem is that documents are stored on instance-local storage, so once the site scales horizontally, users may land on an instance that does not have the requested file. Amazon EFS is the best fit because AWS documents that EFS provides shared file storage with concurrent access from multiple EC2 instances and is accessible from all Availability Zones in the Region. That lets every web server read the same document set without changing to an object-storage pattern. The cron-based answers introduce delay and inconsistency, and S3 would require a bigger application-path change than the shared file system approach. For the least disruptive and most reliable restoration of access across multiple EC2 instances, mounting a single EFS file system on all instances is the right solution.

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The job runs for about 1 hour and must not be interrupted, so Lambda is not suitable because AWS Lambda has a maximum execution time of 15 minutes. Fargate Spot is also not appropriate because the workload explicitly cannot tolerate interruption. The most cost-effective remaining design is to run the Python script on an EC2 On-Demand instance only during the nightly processing window and automate instance start/stop around the schedule. That avoids paying for compute all day while preserving uninterrupted execution during the job. The Step Functions Express option is not the natural fit for a single long-running Python job of this type. Therefore, an EC2 instance scheduled to run only when needed is the most economical answer among the choices.

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AWSDirect Connectis the best solution for establishing a consistent, low-latency connection from an on-premises data center to AWS without using the public internet. Direct Connect offers dedicated, high-throughput, and low-latency network connections, which are ideal for performance-sensitive applications like a trading platform that processes high volumes of market data and stock trades.

Direct Connect provides a private connection to your AWS VPC, ensuring that data doesn ' t traverse the public internet, which enhances both security and performance consistency.

AWS References:

AWS Direct Connectprovides a dedicated network connection to AWS services with consistent, low-latency performance.

Best Practices for High Performance on AWSfor performance-sensitive workloads like trading platforms.

Why the other options are incorrect:

A. AWS Client VPN: While this offers secure connectivity, it ' s over the public internet and is not designed for the low-latency, high-performance needs of a trading platform.

C. AWS PrivateLink: PrivateLink is used for connecting VPCs and services within AWS, but it is not designed for connecting on-premises data centers to AWS.

D. AWS Site-to-Site VPN: Although this provides secure connectivity, it uses the public internet, which can introduce latency and doesn ' t meet the low-latency requirements of the use case.

Cluster Placement Group: This type of placement group is designed to provide low-latency network performance and high throughput by grouping instances within a single Availability Zone. It is ideal for applications that require tightly coupled node-to-node communication.

Configuration:

When launching EC2 instances, specify the option to launch them in a cluster placement group.

This ensures that the instances are physically located close to each other, reducing latency and increasing network throughput.

Benefits:

Low-Latency Communication: Instances in a cluster placement group benefit from enhanced networking capabilities, enabling low-latency communication.

High Network Throughput: The network performance within a cluster placement group is optimized for high throughput, which is essential for HPC workloads.

Edge-Optimized API: Designed for global users by routing requests through CloudFront ' s edge locations, reducing latency.

Content Encoding: Enabling content encoding compresses data, further optimizing performance by decreasing payload size.

Caching: Adding API Gateway caching reduces the number of calls to Lambda and database backends, improving latency.

Reserved Concurrency: Although useful, this does not directly affect latency for transferring static and dynamic content.

AWS API Gateway Edge-Optimized APIs Documentation

A. ElastiCache:Provides in-memory caching, not suitable for persistent, scalable databases.

B. DynamoDB Streams + Lambda:Manages replication manually, increasing latency and operational complexity.

C. Aurora Global Database:Provides high availability but does not support active-active configuration.

D. DynamoDB Global Tables:Provides active-active configuration and sub-second RPO.

AWS Secrets Manager is the managed service for securely storing, rotating, and retrieving database credentials. When you store Aurora credentials in Secrets Manager and enable automatic rotation, Secrets Manager updates the credentials in both the database and the stored secret.

Each Lambda function version or alias can call Secrets Manager at runtime to retrieve the current secret value, so even older deployed Lambda versions that use an alias will always obtain the most recent credentials without redeployment.

Why others are not suitable:

B: Embeds credentials in code, requiring redeployment on every rotation and violating security best practices.

C: Environment variables are version-specific; old aliases would continue using outdated values unless you redeploy or change them.

D: Parameter Store can store and rotate secrets but is less integrated for database credential rotation than Secrets Manager; Secrets Manager is the purpose-built minimal-overhead choice here.

Amazon RDS does not support enabling encryption at rest on an existing unencrypted DB instance. To encrypt an existing RDS instance’s data at rest, the recommended method is to:

Take a snapshot of the unencrypted DB instance.

Create an encrypted copy of the snapshot using AWS KMS. This encrypted snapshot contains the existing data encrypted at rest.

Restore a new DB instance from the encrypted snapshot. This new instance will have encryption at rest enabled.

Additionally, to manage encryption keys securely, companies can use customer managed keys (CMKs) in AWS Key Management Service (KMS). CMKs provide greater control over key management policies, rotation, and usage permissions compared to default AWS managed keys. Using a CMK allows customization of access control and auditability.

Option A is incorrect because you cannot enable encryption directly on a snapshot; you must create an encrypted copy. Option C is invalid because encryption cannot be enabled by modifying an existing instance’s configuration. Option D refers to the default AWS managed key, which is less flexible than customer managed keys.

For a MySQL database experiencing high write operations, using Amazon RDS with Provisioned IOPS (io1 or io2) SSD storage is recommended to achieve consistent and low-latency performance. Provisioned IOPS allows you to specify a desired IOPS rate, which is crucial for write-intensive workloads.

Monitoring write operation metrics through Amazon CloudWatch enables you to observe performance and adjust the provisioned IOPS as needed to meet application demands.

AWS Storage Gateway Tape Gateway provides a seamless way to migrate backup data to AWS without requiring changes to the backup software. Migrating to S3 Glacier Deep Archive ensures long-term, cost-effective storage for data that rarely needs retrieval.

Option A:S3 Standard-IA is more expensive than Glacier for long-term storage.

Option B and D:Glacier Flexible Retrieval is costlier than Glacier Deep Archive for archival use cases with low retrieval frequency.

AWS Documentation References:

AWS Storage Gateway Tape Gateway

S3 Glacier Storage Classes