To break down consolidated billing costs by "product line" using tags, the company must use cost allocation tags and ensure those tags are activated for billing at the payer/management level. Because the teams have already applied user-defined tags to the compute resources across accounts, the correct approach is to (1) use those user-defined tags in the billing tools and (2) activate them so they appear in cost and usage reporting for consolidated billing.
B is required because cost reporting by product line depends on selecting the user-defined cost allocation tag key (for example, ProductLine=...) in AWS billing and cost management tooling (such as Cost Explorer reports). AWS-generated tags are not what teams typically use for chargeback by business dimension, and the scenario explicitly says teams tagged resources (i.e., user-defined).
E is required because in an AWS Organizations consolidated billing setup, cost allocation tag activation is handled centrally from the Organizations management account so that the tags can be used consistently in consolidated cost views and reporting. Activating tags centrally ensures the billing system recognizes and includes those tag dimensions in cost allocation data.
Option C is not relevant to billing breakdown; Resource Groups is for organizing and operating on resources, not for consolidated billing allocation. Option D is unnecessary in this scenario's intended best practice because activation for consolidated billing reporting is done from the management account to standardize reporting. In short, you tag resources (already done), then activate the tag for billing and use it in billing reports to see cost by product line across accounts and Regions.
Therefore, B and E together meet the requirement with the correct billing-focused steps.

The requirements are high availability, scalability for traffic spikes, and least administrative overhead. Option C matches established AWS reference patterns: use an Application Load Balancer + Auto Scaling group for the web tier and a managed database service with Multi-AZ for the database tier.
For the web application, placing EC2 instances in an Auto Scaling group across multiple Availability Zones allows the fleet to scale out automatically during the promotional event and to scale in afterward, preserving performance while controlling cost. The Application Load Balancer distributes requests across instances and AZs and removes unhealthy instances from rotation, improving availability.
For the database, Amazon RDS Multi-AZ is the simplest managed HA solution for relational engines, including SQL Server. Multi-AZ provides synchronous replication to a standby instance in another Availability Zone and supports automatic failover. This reduces administrative effort because AWS handles replication, failover orchestration, backups, and underlying infrastructure maintenance within service capabilities. It also ensures database availability during instance or AZ-level disruptions.
Option A is problematic because RDS for SQL Server does not use read replicas as the primary HA mechanism the way Aurora does, and focusing on replicas does not directly address HA in the simplest supported form. Option B and D rely on self-managed SQL Server on EC2, which requires significant operational overhead (patching, backups, replication setup, failover testing, monitoring, and recovery). Option B also suggests multi-Region replication, which adds complexity and is not required to meet "highly available and scalable" for a single-event scale scenario.
Therefore, C provides scalable compute with managed load balancing and managed database high availability, meeting the requirements with the least operational burden.

AWS recommends Auto Scaling with dynamic scaling policies to handle unpredictable workload spikes.
Target tracking scaling policies automatically adjust capacity based on defined metrics such as average CPU utilization or request count per target. This approach ensures applications maintain responsiveness without overprovisioning. Scheduled scaling (options B and C) is only effective when traffic patterns are predictable, which is not the case here. Reserved Instances or Spot Instances also do not address sudden demand changes effectively. Replacing the ALB with an NLB (D) does not solve latency caused by EC2 instance capacity.
Therefore, using EC2 instances in an Auto Scaling group with a target tracking scaling policy is the most effective and cost-efficient solution for unpredictable, short-lived traffic spikes.
References:* Amazon EC2 Auto Scaling User Guide - Target tracking scaling policies* AWS Well- Architected Framework - Performance Efficiency Pillar: Elasticity and scaling

TooManyRequestsException errors occur when Lambda exceeds concurrency limits. The recommended pattern is to use Amazon SQS with Lambda to decouple and buffer traffic, ensuring that bursts of requests are queued and processed smoothly. Enabling provisioned concurrency for Lambda ensures that functions are pre- initialized and ready to handle spikes in load with low latency. Step Functions (B) is designed for workflow orchestration, not high-throughput request buffering. SNS with Lambda (C) does not provide buffering and may overwhelm Lambda during bursts. Reserved concurrency (D) limits function scaling instead of improving resilience. Therefore, option A provides a scalable and resilient solution, minimizing errors during traffic surges.
References:* AWS Lambda Developer Guide - Provisioned concurrency and scaling with SQS* Amazon SQS User Guide - Using Lambda with Amazon SQS* AWS Well-Architected Framework - Reliability Pillar

* A. Aurora MySQL: Handles OLTP workloads efficiently with built-in replication and auto-scaling capabilities.
* B. EC2 with MySQL: Requires heavy manual maintenance and does not scale seamlessly.
* C. RDS for MySQL: Limited in auto-scaling compared to Aurora.
* D. Redshift: Primarily for OLAP, not suitable for OLTP workloads.
References: Amazon Aurora