Option B is the correct solution because Amazon Bedrock evaluation jobs are purpose-built to assess prompt effectiveness, model behavior, and response quality in a repeatable and automated manner. Evaluation jobs support both quantitative metrics and LLM-based judgment, making them suitable for detecting subtle response quality regressions that simple sentiment or latency metrics cannot capture.

By using custom prompt datasets, the company can consistently test multiple prompt templates and model configurations against the same inputs. This enables accurate comparison across updates and eliminates variability introduced by live traffic sampling. Amazon Bedrock evaluation jobs also support structured scoring outputs, which can be used to enforce objective quality thresholds.

Integrating evaluation jobs directly into AWS CodePipeline ensures that quality checks are automatically triggered whenever prompt templates or configurations change. This creates a gated deployment workflow in which only configurations that meet or exceed the predefined quality threshold are promoted. This directly satisfies the requirement to prevent low-quality configurations from being deployed.

Human reviewers can be incorporated by reviewing evaluation results and scores produced by the jobs, enabling informed feedback without manual data collection. Option A and D rely on custom frameworks and indirect quality signals, increasing complexity and reducing reliability. Option C focuses on operational health rather than response quality.

Therefore, Option B provides the most robust, scalable, and AWS-aligned quality assurance process for Amazon Bedrock–based applications.

Amazon Q Developer Pro provides a built-in administrator dashboard designed specifically for organizational observability. This dashboard provides native visibility into user-level metrics across the entire AWS Organization, allowing administrators to identify active vs. underused subscriptions and recognize power users. Crucially, it tracks high-level usage patterns, including code acceptance metrics (such as lines of code generated and accepted), which is a key requirement for measuring ROI and identifying training needs. Using the built-in dashboard provides the necessary insights with the least operational overhead, as it does not require building custom data pipelines (Option C) or complex log processing architectures (Option D).

The least operational overhead approach is to use managed Amazon Bedrock model evaluation workflows with datasets stored in Amazon S3 , and then publish results into Amazon CloudWatch for dashboards. That is exactly what options B and C combine.

Step B correctly places standardized evaluation inputs in Amazon S3 and focuses on granting the evaluation workflow the right permissions to read those datasets. In practice, the key requirement is controlled access to the S3 objects used as evaluation datasets. Establishing IAM permissions and private access patterns (such as using VPC connectivity patterns where applicable to the or ganization’s networking posture) is aligned with enterprise requirements and avoids building custom storage or data distribution systems for evaluators.

Step C then operationalizes the evaluation lifecycle with minimal infrastructure: a scheduled AWS Lambda function starts evaluation jobs using the S3 dataset location, and a second Lambda function checks job status and pushes results and operational signals to CloudWatch . This meets the platform requirement to surface accuracy metrics in dashboards because CloudWatch metrics/logs can be visualized in dashboards and queried through CloudWatch Logs Insights . It also supports continuous, standardized comparisons across models without requiring developers to run ad-hoc experiments.

The alternatives introduce more operational burden. D and E rely on Amazon SageMaker -based tooling, notebook jobs, and open source evaluation frameworks, which require more environment management, dependency control, scaling considerations, and maintenance over time. A includes CORS, which is primarily a browser-access concern and does not address how Bedrock-managed evaluation jobs securely access S3 in the typical service-to-service pattern.

Therefore, B + C achieves standardized model evaluation, automated scheduling, and dashboard-ready observability with the smallest operations footprint.

Option A is the correct solution because it provides proactive, model-aware token management with fine-grained visibility and alerting, which is required for regulated financial workloads. Amazon Bedrock currently exposes token usage metrics after invocation, but it does not natively enforce proactive, model-specific token limits across multiple applications or business units.

By implementing model-specific tokenizers in AWS Lambda, the company can estimate input and output token usage before sending requests to Amazon Bedrock. This enables early detection of requests that are approaching or exceeding model limits and allows the application to block, truncate, or reroute requests proactively rather than reacting to failures.

Publishing token usage metrics to Amazon CloudWatch enables real-time monitoring and alerting at scale, easily supporting more than 5,000 requests per minute. Storing detailed token usage data in Amazon DynamoDB allows the company to attribute usage and costs to specific applications, teams, or business units—an essential requirement for regulatory reporting and internal chargeback.

Option B is incorrect because Amazon Bedrock Guardrails do not currently provide token quota enforcement or proactive token alerts. Option C is reactive and only analyzes failures after they occur. Option D throttles requests but cannot enforce token-based limits or provide per-model cost attribution.

Therefore, Option A best satisfies proactive alerting, scalability, compliance reporting, and cost allocation requirements with acceptable operational effort.

Option C is the correct solution because AWS AppConfig is purpose-built to manage dynamic application configurations with low latency, strong validation, and minimal operational overhead, which directly matches the company’s requirements.

AWS AppConfig enables the company to centrally manage model selection logic, inference parameters, and customer-tier routing rules without redeploying Lambda functions. By using feature flags, the company can easily perform A/B testing of new models or prompt strategies by gradually rolling out changes to a subset of users or customer tiers. This allows experimentation and controlled releases without code changes.

AppConfig also supports JSON schema validation, which is critical for validating parameters such as temperature, maximum token limits, and other model-specific settings before they are applied. This prevents invalid or unsafe configurations from being deployed and reduces the risk of runtime errors or degraded model behavior in production.

Using the AWS AppConfig Agent allows Lambda functions to retrieve configurations efficiently with built-in caching and polling mechanisms, minimizing latency and avoiding excessive calls to configuration services. This approach scales well for high-throughput, low-latency applications such as GenAI APIs behind Amazon API Gateway.

Option A introduces unnecessary redeployment logic and polling complexity. Option B requires building and maintaining custom configuration access patterns in DynamoDB and does not natively support feature flags or schema validation. Option D adds operational overhead by requiring ElastiCache cluster management and custom validation logic.

Therefore, Option C provides the most scalable, flexible, and low-maintenance solution for dynamic model switching, A/B testing, and safe configuration management in a GenAI application.