Score Higher on Verified AIP-C01 | AWS Certified Generative AI Developer - Professional Exam Questions with Answers

Option C best satisfies the requirements because it combines application-aware observability, metric baselining, anomaly detection, and correlated alerting using fully managed AWS services with minimal operational overhead. Amazon CloudWatch Application Insights is designed to automatically monitor application health by analyzing metrics, logs, and events across EC2-based workloads. This aligns directly with the need to detect intermittent performance issues and deviations from expected behavior.

By publishing custom metrics using the CloudWatch embedded metric format, the application can track generative AI–specific signals such as recommendation quality indicators, token usage, request volume, and response latency from Amazon Bedrock foundation model calls. Adding dimensions such as request type or user segment enables fine-grained visibility into which workloads or customer groups are impacted when recommendation quality degrades.

A critical requirement is detecting degradation compared to established baselines and generating alerts within 10 minutes. CloudWatch anomaly detection automatically builds statistical models of normal behavior for time-series metrics and flags deviations without requiring manually tuned thresholds. This capability is well suited for monitoring foundation model behavior, which can vary subtly over time. When anomalies are detected, CloudWatch alarms can trigger notifications with contextual metric data quickly, meeting the alerting requirement.

CloudWatch Logs Insights complements the metric-based view by enabling log pattern analysis and correlation. Engineers can query application logs and model response logs to identify recurring error patterns or shifts in output behavior that explain why recommendations no longer align with user preferences. Application Insights further correlates metrics and logs to surface probable root causes, reducing mean time to resolution.

The other options lack one or more critical elements. Option A focuses on infrastructure-level metrics without baseline anomaly detection. Option B emphasizes tracing and auditing but does not provide automated performance deviation analysis. Option D offers flexibility but requires significantly more development and operational effort than a native CloudWatch-based solution.

Option A is the best solution because it satisfies streaming, token control, and retry requirements while keeping operational overhead low by using fully managed, serverless AWS services. Amazon API Gateway HTTP APIs provide a lightweight, cost-effective front door for APIs and integrate cleanly with AWS Lambda for request processing and security controls.

AWS Lambda response streaming allows the API to begin returning content to the client as soon as partial model output is available, reducing perceived latency and improving user experience for long responses. Using Lambda as the integration layer also provides a centralized place to enforce token-aware request handling, such as rejecting oversized requests, truncating optional context, or applying consistent limits across users and tenants to manage compute usage.

Retry logic is best handled in the client or integration layer for transient failures such as timeouts and throttling. Lambda can implement controlled retries with exponential backoff and jitter, while API Gateway timeouts help bound request lifetimes and prevent hung connections from consuming resources indefinitely. Because the model service is managed, the company avoids infrastructure management and focuses only on request shaping, safety, and resiliency behavior.

Option B is not suitable because client-side polling is not true streaming, front-end token enforcement is insecure and inconsistent, and API Gateway does not provide model-aware retry behavior on its own. Option C introduces container hosting and scaling complexity, which increases operational overhead compared to serverless. Option D can work, but REST APIs are generally heavier than HTTP APIs for this pattern and do not reduce overhead compared to Option A.

Therefore, Option A provides the required streaming and resiliency capabilities with the least infrastructure management effort.

Option D is the best fit because it implements a reliable event-driven MLOps workflow that automates retraining and redeployment with clear orchestration, auditability, and production-grade error handling. The requirement is explicit: whenever a new file is uploaded to Amazon S3, the system must retrain and then redeploy the model used by a web application. A common AWS pattern is to use an S3 event notification to trigger an AWS Lambda function, which then starts a controlled workflow. In option D, Lambda serves as the event handler that reacts immediately to the S3 upload event and passes the necessary context (bucket, object key, dataset version) into an AWS Step Functions Standard state machine.

Step Functions Standard is appropriate for model retraining pipelines because training and deployment steps can be long-running and benefit from durable state, retries, and failure handling. It provides execution history, making it easier to troubleshoot why a particular retraining run failed and to prove which dataset version produced which model version. This operational visibility is critical when the dataset is “growing rapidly” and retraining is frequent.

Within the workflow, Amazon SageMaker Pipelines is the right service to run the ML lifecycle stages in a repeatable way: data processing (if needed), training, evaluation/quality checks, model registration, and deployment to an endpoint used by the application. SageMaker Pipelines is purpose-built for CI/CD-style ML, supporting automated redeployments when a new approved model artifact is produced. By calling a pipeline execution from Step Functions, the company can add governance gates (for example, only deploy if evaluation metrics meet thresholds), and can apply consistent rollback and notification steps when deployment fails.

The other options are weaker: A confuses inference with retraining and does not provide deployment orchestration. B adds unnecessary webhook complexity and describes an awkward event bus configuration. C introduces Autopilot/Data Wrangler, which may be useful but adds extra moving parts and is not required to meet the trigger-and-redeploy requirement.