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104. Frage
A retail company runs an application that makes product recommendations to customers on the company's website. The application uses Amazon Bedrock to generate recommendations by dynamically constructing prompts and sending them to foundation models (FMs). A GenAI developer has deployed an update to the application that instructs the FM to include a specific promotional message when the FM generates a response to prompts. When the developer tests the application, the promotional message does not always appear in the responses. When the promotional message does appear in the responses, it does not always flow with the rest of the text. The GenAI developer must ensure that the promotional message always appears in the FM responses. Which solution will meet this requirement?
Antwort: A
Begründung:
When a foundation model fails to include specific required content or fails to integrate it coherently, prompt engineering techniques like output indicators or " wrappers " are highly effective. By explicitly defining where the promotional message should appear (e.g., " The response must end with the following message:
[PROMO TEXT] " ) or providing an example output structure, the developer reinforces the constraint within the model ' s generation path. This is more direct and less computationally expensive than generating multiple variants and reranking them (Option B) or adding complex post-processing layers (Option C). Guardrails (Option A) are intended for filtering harmful content rather than enforcing specific promotional copy insertion.
105. Frage
An ecommerce company is using Amazon Bedrock to build a generative AI (GenAI) application. The application uses AWS Step Functions to orchestrate a multi-agent workflow to produce detailed product descriptions. The workflow consists of three sequential states: a description generator, a technical specifications validator, and a brand voice consistency checker. Each state produces intermediate reasoning traces and outputs that are passed to the next state. The application uses an Amazon S3 bucket for process storage and to store outputs.
During testing, the company discovers that outputs between Step Functions states frequently exceed the 256 KB quota and cause workflow failures. A GenAI Developer needs to revise the application architecture to efficiently handle the Step Functions 256 KB quota and maintain workflow observability. The revised architecture must preserve the existing multi-agent reasoning and acting (ReAct) pattern.
Which solution will meet these requirements with the LEAST operational overhead?
Antwort: A
Begründung:
Option B is the best solution because it directly addresses the Step Functions 256 KB state payload quota by externalizing large intermediate artifacts to Amazon S3 and passing only lightweight references (URIs/keys) between states. This is a standard AWS pattern for workflows that produce large intermediate results, and it avoids introducing additional databases, compression logic, or cross-state-machine coordination that increases operational overhead.
In a multi-agent ReAct workflow, intermediate reasoning traces can be verbose and grow quickly as each agent produces chain-of-thought style artifacts, structured outputs, and supporting evidence. Step Functions is designed to orchestrate state transitions and pass JSON payloads, but large payloads should be stored outside the state machine and referenced by pointer values. Using Amazon S3 for intermediate outputs is operationally efficient because the application already uses S3 for storage, and S3 provides durable, low-cost storage with simple access patterns.
ResultPath and ResultSelector allow each state to store or reshape results so that only the required reference fields (such as s3Uri, object key, metadata, trace IDs) are forwarded to subsequent states. This preserves observability because the workflow can still log trace references, correlate steps with S3 objects, and store structured metadata for debugging. It also preserves the sequential validation design, keeping the existing ReAct pattern intact while preventing failures due to oversized payloads.
Option A adds additional services and read/write patterns that increase operational complexity. Option C introduces custom compression/decompression logic that is fragile, adds latency, and complicates troubleshooting. Option D increases orchestration overhead by splitting workflows and coordinating with events, which makes debugging harder and increases failure modes.
Therefore, Option B meets the payload limit requirement while keeping the architecture simple and observable.
106. Frage
A specialty coffee company has a mobile app that generates personalized coffee roast profiles by using Amazon Bedrock with a three-stage prompt chain. The prompt chain converts user inputs into structured metadata, retrieves relevant logs for coffee roasts, and generates a personalized roast recommendation for each customer.
Users in multiple AWS Regions report inconsistent roast recommendations for identical inputs, slow inference during the retrieval step, and unsafe recommendations such as brewing at excessively high temperatures. The company must improve the stability of outputs for repeated inputs. The company must also improve app performance and the safety of the app's outputs. The updated solution must ensure 99.5% output consistency for identical inputs and achieve inference latency of less than 1 second. The solution must also block unsafe or hallucinated recommendations by using validated safety controls.
Which solution will meet these requirements?
Antwort: B
Begründung:
Option A is the only choice that simultaneously addresses all three requirements: (1) higher output consistency for identical inputs, (2) sub-1-second performance, and (3) validated safety controls that block unsafe or hallucinated recommendations.
Provisioned throughput in Amazon Bedrock reserves capacity for the chosen model, which helps stabilize latency and reduces the chance of throttling or variable response times across Regions. This is important for a mobile app with strict latency goals and users distributed across multiple Regions. While provisioned throughput primarily improves performance predictability, it also reduces variability caused by contention during peak demand.
Amazon Bedrock guardrails provide validated safety controls to filter or block unsafe content. Semantic denial rules are appropriate for preventing dangerous brewing guidance (for example, excessively high temperatures) and for reducing hallucinated instructions that violate safety policies. Guardrails can be enforced consistently regardless of prompt-chain complexity, providing a uniform safety layer around the model outputs.
Amazon Bedrock Prompt Management supports controlled prompt versioning and approval workflows. By standardizing prompts, controlling changes, and ensuring the same prompt version is used for identical inputs, the company improves output stability and reduces drift caused by unmanaged prompt edits. Combined with strict configuration control (including fixed inference parameters such as temperature where appropriate), this improves repeatability and increases the likelihood of achieving the 99.5% consistency target.
Option B improves observability and experimentation but does not provide strong safety enforcement or latency stabilization. Option C improves performance through caching and tracing but does not provide validated safety controls and does not directly address cross-Region output consistency. Option D may improve retrieval but does not enforce safety controls or ensure repeatable outputs.
Therefore, Option A best meets the stability, performance, and safety requirements using AWS-native controls.
107. Frage
An insurance company uses existing Amazon SageMaker AI infrastructure to support a web-based application that allows customers to predict what their insurance premiums will be. The company stores customer data that is used to train the SageMaker AI model in an Amazon S3 bucket. The dataset is growing rapidly. The company wants a solution to continuously re-train the model. The solution must automatically re-train and re- deploy the model to the application when an employee uploads a new customer data file to the S3 bucket.
Which solution will meet these requirements?
Antwort: C
Begründung:
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.
108. Frage
A healthcare company uses Amazon Bedrock to deploy an application that generates summaries of clinical documents. The application experiences inconsistent response quality with occasional factual hallucinations.
Monthly costs exceed the company's projections by 40%. A GenAI developer must implement a near real- time monitoring solution to detect hallucinations, identify abnormal token consumption, and provide early warnings of cost anomalies. The solution must require minimal custom development work and maintenance overhead.
Which solution will meet these requirements?
Antwort: C
Begründung:
Option C is the correct solution because it provides near real-time monitoring, hallucination detection, and cost anomaly awareness using built-in Amazon Bedrock and Amazon CloudWatch capabilities, with minimal custom development.
By configuring Amazon Bedrock invocation logging with text output logging, the company captures detailed prompt and response data for auditing and analysis without building custom logging pipelines. This data is stored in Amazon S3, providing durable storage for compliance and retrospective investigation.
Using Amazon Bedrock guardrails with contextual grounding checks allows the application to automatically detect hallucinations by verifying whether generated summaries are grounded in the provided clinical documents. This is the AWS-recommended approach for hallucination detection in RAG and summarization workloads and avoids the need to maintain custom evaluation models or pipelines.
Creating Amazon CloudWatch anomaly detection alarms for InputTokenCount and OutputTokenCount metrics enables automatic detection of abnormal token usage patterns that often correlate with runaway prompts, inefficient summarization, or prompt injection attempts. Anomaly detection adapts dynamically to usage trends, making it more effective than static thresholds for early cost warnings.
Option A introduces batch analytics with Glue and Athena, which is not near real time and increases operational overhead. Option B requires managing evaluation jobs and Lambda-based notification logic.
Option D focuses on infrastructure-level monitoring and offline dashboards rather than near real-time GenAI quality and cost signals.
Therefore, Option C best meets the requirements with the least operational effort and maintenance overhead.
109. Frage
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