The company is looking for a solution that provides single sign-on (SSO) across multiple AWS accounts while continuing to manage users and groups in their on-premises Active Directory (AD). AWS IAM Identity Center (formerly AWS SSO) is the recommended solution for this type of requirement.

AWS IAM Identity Centerprovides a centralized identity management solution, enabling single sign-on across multiple AWS accounts and other cloud applications. It can integrate with on-premises Active Directory to leverage existing users and groups.

By configuring a two-way forest trust relationship between AWS Directory Service for Microsoft Active Directory and the company ' s on-premises Active Directory, users can be authenticated by their on-premises AD and still access AWS resources through IAM Identity Center. This solution allows centralized management of AWS accounts within AWS Organizations.

The two-way trust allows mutual access between the on-premises AD and the AWS Directory Service. This means that users and groups in the on-premises AD can be used for authentication in AWS IAM Identity Center while maintaining the existing identity management system.

AWS References:

AWS IAM Identity Center Documentation

AWS Directory Service for Microsoft Active Directory Trust Relationships

AWS Directory Service Integration with IAM Identity Center

Why the other options are incorrect:

A. Create an Enterprise Edition Active Directory in AWS Directory Service: This would require setting up a new directory and managing it in AWS, which adds unnecessary overhead. The requirement is to continue using the existing on-premises AD, making this option unsuitable.

C. Use AWS Directory Service and create a two-way trust relationship: While this approach establishes a trust between on-premises AD and AWS Directory Service, it does not address the single sign-on (SSO) requirements across multiple AWS accounts through IAM Identity Center.

D. Deploy an identity provider (IdP) on Amazon EC2: This is more complex than necessary and introduces more management overhead. AWS IAM Identity Center natively supports integration with on-premises Active Directory without requiring a custom IdP.

AWS Key Management Service (AWS KMS) provides multi-Region keys, allowing the same key to be used across Regions without managing your own hardware or key replication.

Multi-Region keys are fully managed and support attribute-based access control (ABAC) using tags and condition keys.

CloudHSM (Options C and D) requires full key lifecycle management, synchronization, and hardware operations, resulting in significantly higher overhead.

Imported key material (Option B) increases key-management responsibility.

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CloudTrail log file validation ensures that the log files have not been altered or deleted after delivery, providing an immutable audit log. Using KMS-managed encryption keys for CloudTrail log files adds another layer of data security, and AWS Config can monitor compliance to ensure this security is always enforced.

Reference Extract:

" CloudTrail log file validation provides assurance about the integrity of CloudTrail logs. Using SSE-KMS encryption and monitoring with AWS Config helps secure and audit logs for compliance. "

Source: AWS Certified Solutions Architect – Official Study Guide, CloudTrail Security section.

AWS recommends using Amazon ElastiCache as an in-memory caching layer in front of relational databases such as Amazon RDS to offload read traffic and significantly improve performance for read-heavy workloads. ElastiCache (Redis OSS) provides microsecond latency and can cache the results of frequent or expensive queries without requiring any change to the underlying database schema or engine. This directly addresses the requirement to improve performance without changing the database structure and with minimal operational overhead, because ElastiCache is a fully managed service (patching, failure detection, replacement, etc., are handled by AWS).

Option A (DynamoDB) would require a full data migration and application changes, including schema and query rewrites.

Option C (Memcached on EC2) introduces additional management overhead for EC2 instances (scaling, patching, HA).

Option D (DAX) is a caching layer only for DynamoDB and cannot be used directly with Amazon RDS.

The company’s requirements are:

Store gigabytes of rarely accessed files daily.

Files must be available within minutes during the first year.

Retain files for 7 years cost-effectively.

The S3 Glacier Instant Retrieval storage class provides low-cost, long-term storage for data accessed occasionally, with millisecond retrieval time. This fits the requirement for availability within minutes during the first year. After one year, transitioning files to S3 Glacier Deep Archive (the lowest cost S3 storage class) with longer retrieval times is cost-effective for data retention over 7 years.

Option B uses S3 Standard and Glacier Flexible Retrieval, which is higher cost during the first year and slower retrieval times. Option C is less cost-efficient because EBS volumes are expensive for rarely accessed data and snapshots incur additional costs. Option D uses EFS, which is designed for low-latency file storage but is more expensive than S3 Glacier classes for long-term archival.

Amazon CloudFront can usemultiple originsin one distribution, including anAmazon S3 bucketfor static content and anApplication Load Balancerfor dynamic content. That lets the company reduce latency globally for both content types while keeping a single front door for users. AWS documentation specifically notes that CloudFront supports both S3 origins and load balancer origins, and a single distribution can include multiple origins. Route 53 can then alias the company’s domain to the CloudFront distribution. The Global Accelerator options are unnecessary here because CloudFront already addresses the performance need for both static and dynamic web delivery in this architecture.

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AWS Secrets Manager natively supports automatic rotation of RDS for Oracle credentials, fully integrating with Amazon RDS. Rotation workflows are managed automatically by the service, eliminating custom scripting and reducing operational effort.

Migrating to EC2 (Option C) requires custom rotation logic. Converting to DynamoDB or Neptune (Options A and D) would require complete database redesign and do not fulfill the requirement to run Oracle.

Amazon CloudFront is a content delivery network (CDN) service that caches copies of your content at edge locations around the world. This helps improve performance by serving content from the edge nearest to the user. However, when the content in Amazon S3 (your origin) is updated, those updates may not immediately reflect on the website if they are cached at the CloudFront edge locations.

The issue described in the question suggests that the CI/CD pipeline is functioning correctly, and updates are being deployed to S3. However, since CloudFront caches this content, the edge locations may still be serving outdated content, causing the updates to not be reflected on the website.

To resolve this issue, you need to invalidate the CloudFront cache. By invalidating the cache, CloudFront will remove the outdated content and retrieve the latest version from the S3 origin.

AWS documentation on this process:

CloudFront cache invalidation allows you to clear items from the cache so that CloudFront retrieves the latest version from the origin. You can create invalidation requests via the AWS Management Console, AWS CLI, or SDKs.

AWS CloudFront Documentation

Why the other options are incorrect:

A. Add an Application Load Balancer: ALBs are used to distribute incoming application traffic and are not relevant to caching or serving content from CloudFront.

B. Add Amazon ElastiCache for Redis or Memcached: This would help in caching database queries but has no relation to static website content hosted on CloudFront and S3.

D. Use AWS Certificate Manager (ACM): ACM is used for managing SSL/TLS certificates and is unrelated to the issue of content not being updated on CloudFront.

The Lambda function already has an execution role, so the correct fix is toadd CloudWatch Logs permissions to that same role. AWS documentation lists the required permissions for a Lambda function to write logs: logs:CreateLogGroup, logs:CreateLogStream, and logs:PutLogEvents. A separate role for the S3 bucket would not help because Lambda uses its own execution role when it runs. An S3 bucket policy is unrelated to CloudWatch Logs logging. The logs:GetLogEvents permission is for reading logs, not writing them. Updating the existing execution role is the least-complex and correct solution.

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The most secure and least operationally intensive method is to configure cross-account access using resource-based policies on the S3 bucket. This allows trusted external AWS accounts (consulting agencies) to securely access the S3 data without the need to manage user credentials or build additional infrastructure.

Options A and B pose security risks. Option D increases operational complexity and violates least privilege by managing external users inside your AWS account.

A. Mutual TLS:Provides secure communication but does not integrate with AWS credential exchange.

B. AWS Signature v4:Requires direct integration with AWS and is less secure for external systems.

C. Kerberos:Not natively supported for AWS API authentication.

D. IAM Roles Anywhere:Enables AWS API access using X.509 certificates without long-term credentials.

Predictive scaling in EC2 Auto Scaling uses machine learning to analyze historical load and forecast future capacity needs, then pre-scales capacity before the demand occurs. It is specifically designed for recurring, cyclical workloads such as weekly batch jobs.

In this scenario:

The workload pattern is known and recurring (weekly scripted jobs).

The company explicitly does not want to manually analyze trends.

They need capacity to be ready 30 minutes before jobs start.

Predictive scaling lets you:

Set the metric (CPU utilization) and target (60%).

Configure the policy to pre-launch instances 30 minutes in advance, satisfying the requirement automatically with minimal operational overhead.

Dynamic scaling (Option A) reacts after CPU increases, not before.

Scheduled scaling (Option B) requires manual analysis and ongoing adjustment of capacity values.

EventBridge + Lambda (Option D) introduces unnecessary custom logic and still reacts after CPU reaches 60%, not 30 minutes before.

The problem is initialization latency from launching instances based on a large AMI. Amazon EBS Fast Snapshot Restore is specifically designed to eliminate the added latency that normally occurs when volumes are created from snapshots and blocks are initialized lazily. By enabling FSR for the snapshot used by the AMI, the company can launch instances with fully initialized volumes more quickly, which helps the Auto Scaling group react faster to sudden demand. The other options do not directly solve the snapshot-initialization delay. Therefore, enabling Fast Snapshot Restore is the best performance optimization for this scenario.

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The application is described as stateful, which means user sessions or state are likely stored in memory on the application server. When migrating to an Auto Scaling group behind a load balancer, a consistent user experience typically requires session stickiness, so a user’s subsequent requests continue to be routed to the same backend instance that holds their session state (unless the application is refactored to externalize session state, which is not stated here). For HTTP/HTTPS web applications, an Application Load Balancer (ALB) is the correct Elastic Load Balancing choice because it operates at Layer 7 and provides built-in support for cookie-based sticky sessions.

On the database side, Aurora PostgreSQL supports scaling read capacity by adding Aurora Replicas, and Aurora Auto Scaling can automatically adjust the number of replicas based on demand. This is the intended mechanism to scale the database tier for growing read traffic while preserving write consistency through a single writer. “Aurora writers” do not scale horizontally the same way; Aurora provides one writer endpoint at a time (with fast failover), so auto-scaling “writers” is not the right construct for demand-based scaling.

Option C combines the correct load balancer type (ALB) with stickiness and the correct database scaling mechanism (Aurora Replicas with auto scaling). Option A uses an NLB, which is Layer 4 and does not provide the same Layer 7 sticky session behavior expected for a web application pattern. Options B and D incorrectly focus on scaling Aurora writers, which does not address scaling demand patterns as effectively as scaling read replicas.

Therefore, C provides the most consistent user experience for a stateful web tier and enables the database tier to scale using Aurora Replica auto scaling.

Amazon S3 Express One Zone provides single-digit millisecond latency and high throughput, making it ideal for ML workloads that require multiple compute nodes and fast access. It is also more cost-effective than traditional file or block storage for temporary, high-speed needs.

Explanation (AWS Docs):

To allow private subnets to access the internet, deploy NAT gateways in a public subnet in each AZ for high availability. NAT instances are less scalable and less fault-tolerant.

“To create a highly available architecture, create a NAT gateway in each Availability Zone and configure your routing to use it.”

— NAT Gateway Overview

To track costs associated with different teams within a single AWS account, the company should implement user-defined cost allocation tags.

Tagging Resources with CostCenter: Assign the CostCenter tag to all resources, specifying the appropriate value for each team. This practice enables the organization to categorize and track AWS costs effectively.

Activating the CostCenter Tag: After tagging resources, activate the CostCenter tag in the AWS Billing and Cost Management console. Activation is necessary for the tags to appear in cost allocation reports and AWS Cost Explorer.

By following these steps, the company can generate detailed billing reports that break down costs by team, facilitating better cost management and accountability.

A VPC gateway endpoint for Amazon S3 enables private connectivity to S3 without routing traffic through a NAT gateway or over the internet, eliminating NAT gateway costs. This solution is secure and redundant, as S3 endpoints are highly available by design.

AWS Documentation Extract:

" A gateway VPC endpoint enables you to privately connect your VPC to supported AWS services without requiring a NAT gateway or internet gateway. "

(Source: Amazon VPC documentation, Gateway Endpoints)

A: NAT instances still incur operational overhead and costs.

B: Internet gateway exposes resources and does not provide private access.

D: Direct Connect is for hybrid networking, not for cost-efficient S3 access.

To achieve a 60-second RTO, pre-warming the DR environment (including running EC2 instances and Route 53 health checks) is essential. Active/passive failover using Route 53 with health checks ensures fast redirection when the primary Region becomes unavailable. S3 cross-region replication ensures document availability.

Why Option C is Correct:

S3 Access Points: Provide scalable management of access to large datasets with specific permissions for individual prefixes.

Dynamic Prefixes: Access points simplify managing access to a growing number of prefixes without relying solely on a single bucket policy.

Fine-Grained Control: Resource-based permissions on access points enforce prefix-level isolation effectively.

Why Other Options Are Not Ideal:

Option A: Using deny/allow bucket policies introduces complexity and is less scalable for dynamic prefixes.

Option B: Encryption ensures data security but does not address access management.

Option D: Pre-signed URLs are temporary and not suitable for managing access at scale.

AWS References:

Amazon S3 Access Points:AWS Documentation - S3 Access Points