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| Section | Weight | Objectives |
|---|---|---|
| AI Safety, Security, and Governance | 20% | - Implement safety and guardrails
|
| Implementation and Integration | 26% | - Integrate foundation models into applications and workflows
|
| Operational Efficiency and Optimization for GenAI Applications | 12% | - Deploy and manage scalable solutions
|
| Testing, Validation, and Troubleshooting | 11% | - Troubleshoot and resolve issues
|
| Foundation Model Integration, Data Management, and Compliance | 31% | - Manage data for generative AI applications
|
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NEW QUESTION # 66
A company deploys multiple Amazon Bedrock-based generative AI (GenAI) applications across multiple business units for customer service, content generation, and document analysis. Some applications show unpredictable token consumption patterns. The company requires a comprehensive observability solution that provides real-time visibility into token usage patterns across multiple models. The observability solution must support custom dashboards for multiple stakeholder groups and provide alerting capabilities for token consumption across all the foundation models that the company's applications use.
Which combination of solutions will meet these requirements with the LEAST operational overhead? (Select TWO.)
Answer: B,E
Explanation:
The combination of Options C and D delivers comprehensive, real-time observability for Amazon Bedrock workloads with the least operational overhead by relying on native integrations and managed services.
Amazon Bedrock publishes built-in CloudWatch metrics for model invocations and token usage. Option C leverages these native metrics directly, allowing teams to build centralized CloudWatch dashboards without additional data pipelines or custom processing. CloudWatch alarms provide threshold-based alerting for token consumption, enabling proactive cost and usage control across all foundation models. This approach aligns with AWS guidance to use native service metrics whenever possible to reduce operational complexity.
Option D complements CloudWatch by enabling advanced, stakeholder-specific visualizations through Amazon Managed Grafana. The zero-ETL integration allows Bedrock and CloudWatch metrics to be visualized directly in Grafana without building ingestion pipelines or managing storage layers. Grafana dashboards are particularly well suited for serving different audiences, such as engineering, finance, and product teams, each with customized views of token usage and trends.
Option A introduces unnecessary complexity by adding a business intelligence layer that is better suited for historical analytics than real-time operational monitoring. Option B is useful for deep log analysis but requires query maintenance and does not provide efficient real-time dashboards at scale. Option E involves multiple services and custom data flows, significantly increasing operational overhead compared to native metric- based observability.
By combining CloudWatch dashboards and alarms with Managed Grafana's zero-ETL visualization capabilities, the company achieves real-time visibility, flexible dashboards, and automated alerting across all Amazon Bedrock foundation models with minimal operational effort.
NEW QUESTION # 67
A media company is launching a platform that allows thousands of users every hour to upload images and text content. The platform uses Amazon Bedrock to process the uploaded content to generate creative compositions.
The company needs a solution to ensure that the platform does not process or produce inappropriate content.
The platform must not expose personally identifiable information (PII) in the compositions. The solution must integrate with the company's existing Amazon S3 storage workflow.
Which solution will meet these requirements with the LEAST infrastructure management overhead?
Answer: A
Explanation:
Option D is the correct solution because it relies primarily on managed, purpose-built AWS services and minimizes custom infrastructure and model management. Amazon Bedrock guardrails provide native, configurable content safety controls that can block or redact disallowed content before or after model inference. This directly ensures that the platform does not process or produce inappropriate outputs while maintaining low operational overhead.
Using Amazon Comprehend PII detection as a preprocessing step integrates cleanly with an Amazon S3- based ingestion workflow. Comprehend is a fully managed service that detects and optionally redacts PII in text without requiring custom models or pipelines. This ensures that sensitive information is removed before content is passed to Amazon Bedrock for generation.
Amazon Rekognition image moderation is purpose-built for detecting unsafe or inappropriate visual content and integrates naturally into Step Functions workflows. Step Functions provides orchestration without requiring servers or long-running infrastructure, allowing the company to integrate text and image moderation steps in a clear, auditable pipeline.
Option A introduces redundant monitoring logic and alarms that do not directly enforce content safety. Option B requires building and maintaining custom SageMaker models, increasing complexity and operational burden. Option C applies moderation at authentication time and uses services like Textract that are not designed for content moderation, increasing latency and management overhead.
Therefore, Option D best satisfies content safety, PII protection, S3 integration, and minimal infrastructure management requirements.
NEW QUESTION # 68
A financial services company is using Amazon Bedrock to deploy a GenAI application across multiple business units. The company must ensure that all prompts that are used with the application ' s FMs follow regulatory compliance standards and maintain consistent formatting.
The company must implement a solution that provides version control for prompt templates, requires approval workflows for new prompts, and maintains detailed audit trails of all prompt usage and modifications.
Which combination of solutions will meet these requirements? (Select TWO.)
Answer: C,E
Explanation:
Options A and C provide the most AWS-native governance approach. Amazon Bedrock Prompt Management supports reusable parameterized prompts and explicit prompt versions, allowing teams to preserve approved configurations and deploy stable versions instead of uncontrolled working drafts. An organizational approval gate can therefore be applied before a new prompt version becomes production-approved. AWS CloudTrail records Amazon Bedrock API activity, including the identity making the request, the operation performed, time, source information, and other event details, providing the required audit trail for Bedrock resource modifications and use. CloudWatch can complement this with operational usage monitoring and reporting.
Option B recreates prompt governance through S3, Lambda, and SNS. Option D uses general-purpose configuration storage rather than Bedrock-native prompt management. Option E adds substantial custom state and filtering logic without providing the same managed prompt-version lifecycle. AWS Documentation
NEW QUESTION # 69
A healthcare company uses a multi-agent system on Amazon Bedrock AgentCore. The system uses multiple FMs to process 2,000 medical report documents daily. The documents range from 5 to 50 pages each.
The company discovers that the system is not performing complete analysis on documents that exceed 30 pages. Documents over 30 pages miss critical information from later sections. The results are truncated, but there are no explicit errors.
The company must resolve this issue while maintaining response times under 10 seconds. The company must keep processing costs low.
Which solution will meet these requirements?
Answer: A
Explanation:
Option B is correct because the symptoms indicate context-window pressure rather than a compute-memory or Lambda-timeout problem. Large documents should be split into manageable chunks while preserving semantic continuity across boundaries. Amazon Bedrock Knowledge Bases supports chunking strategies, including semantic chunking and overlap-oriented approaches, so relevant context can be retained without sending an entire long report to the model at once. Monitoring InputTokenCount provides evidence of context utilization and helps identify when requests approach model limits. Diagnostic logging can then locate where truncation occurs. Option A increases timeouts but does not enlarge the model context window. Option C adds sequential orchestration that can jeopardize the 10-second target. Option D increases infrastructure resources, but host memory does not change a foundation model's maximum context window and adds unnecessary cost. AWS Documentation
NEW QUESTION # 70
A healthcare company is developing an application to process medical queries. The application must answer complex queries with high accuracy by reducing semantic dilution. The application must refer to domain- specific terminology in medical documents to reduce ambiguity in medical terminology. The application must be able to respond to 1,000 queries each minute with response times less than 2 seconds.
Which solution will meet these requirements with the LEAST operational overhead?
Answer: B
Explanation:
Option B provides the least operational overhead because it keeps the solution primarily inside managed Amazon Bedrock capabilities, minimizing custom orchestration code and infrastructure to operate. The core requirements are domain grounding, reduced semantic dilution for complex questions, and consistent low- latency responses at high request volume. A Bedrock knowledge base is purpose-built for Retrieval Augmented Generation by ingesting domain documents, chunking content, generating embeddings, and retrieving the most relevant passages at runtime. This directly addresses the need to reference domain-specific medical terminology from authoritative documents to reduce ambiguity and improve factual accuracy.
Reducing semantic dilution typically requires improving the retrieval query so that the retriever focuses on the most relevant concepts, especially for long or multi-intent questions. Enabling query decomposition allows the system to break a complex medical query into smaller, more targeted sub-queries. This increases retrieval precision and recall for each sub-question, which helps the model generate a more accurate synthesized response grounded in the retrieved medical context.
Amazon Bedrock Flows provide a managed way to orchestrate multi-step generative AI workflows, such as preprocessing the input, performing retrieval against the knowledge base, invoking a foundation model, and formatting the final response. Because flows are managed, the company avoids maintaining custom state machines, multiple Lambda functions, or bespoke routing logic. This reduces operational overhead while still supporting repeatable, observable execution.
Compared with the alternatives, option A introduces an agent plus API Gateway routing and multiple model choices, increasing configuration and runtime complexity. Option C requires hosting and scaling custom models on SageMaker AI, which adds significant operational burden and latency risk. Option D relies on multiple Lambda functions orchestrated by an agent, which adds more moving parts and increases cold-start and integration overhead. Option B most directly meets the requirements with the smallest operational footprint.
NEW QUESTION # 71
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