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問題 #63
An ecommerce company operates a global product recommendation system that needs to switch between multiple foundation models (FMs) in Amazon Bedrock based on regulations, cost optimization, and performance requirements. The company must apply custom controls based on proprietary business logic, including dynamic cost thresholds, AWS Region-specific compliance rules, and real-time A/B testing across multiple FMs.
The system must be able to switch between FMs without deploying new code. The system must route user requests based on complex rules including user tier, transaction value, regulatory zone, and real-time cost metrics that change hourly and require immediate propagation across thousands of concurrent requests.
Which solution will meet these requirements?
答案：C
解題說明：
Option C cleanly separates rapidly changing routing configuration from application code. AWS AppConfig Agent is the AWS-recommended mechanism for retrieving configuration data and maintains a local cache while asynchronously polling AppConfig for updates. In Lambda environments, the extension periodically refreshes configuration in the background and lets functions retrieve cached values locally with very low latency. The Lambda function can therefore evaluate proprietary rules involving user tier, transaction value, Region, compliance constraints, and current model costs without redeployment. Model identifiers and routing thresholds can change independently through AppConfig while API Gateway exposes one stable application endpoint. Option A requires environment-variable modifications and Lambda configuration updates. Option B makes API Gateway transformation templates responsible for complex business decisions. Option D incorrectly couples model-routing decisions with authorization logic and requires separate model-specific Lambda functions, reducing flexibility. AWS Documentation
問題 #64
A healthcare company is building a generative AI (GenAI) application to answer patient questions based on patient medical records and published treatment guidelines. To comply with healthcare regulations, the application must provide transparent citations for all information sources. The application must also maintain an audit trail for all data access and ensure that responses reference only authorized medical information.
Which solution will meet these requirements with the LEAST operational overhead?
答案：A
解題說明：
Option B uses managed retrieval and observability instead of custom citation infrastructure. Amazon Bedrock Knowledge Bases can connect applications to controlled enterprise data sources and perform retrieval- augmented generation. The RetrieveAndGenerate workflow returns generated answers together with citations to the specific source chunks used, directly addressing the transparency requirement. Access to the knowledge base and its backing resources can be governed through AWS authorization controls so the application retrieves only permitted medical information. Bedrock invocation logging and CloudWatch observability can record invocation activity, inputs, outputs, usage, and operational events to support investigation and auditing.
Option A does not supply the required source-grounded retrieval and citations by itself. Option C requires a custom external vector system and Lambda-based citation tracking. Option D adds fine-tuning and custom attribution infrastructure even though neither is necessary for a source-grounded healthcare RAG application.
AWS Documentation
問題 #65
A company is developing three specialized NLP models that support a customer service application. One model categorizes each customer's specific issue. Another model extracts key information from the customer interactions. The third model generates responses. The company must ensure that the application achieves at least 95% accuracy for all tasks. The application must handle up to 500 concurrent requests and respond in less than 500 ms during daily 2-hour peak usage periods. The company must ensure that the application optimizes resource usage during periods of low demand between usage spikes. Which solution will meet these requirements?
答案：D
解題說明：
Amazon SageMaker Serverless Inference is specifically designed for applications that experience intermittent or bursty traffic. It automatically scales compute capacity based on the number of requests and scales down to zero when there is no traffic, satisfying the requirement to optimize resource usage during low demand. To meet the 500 ms latency requirement during peak periods and avoid " cold start " delays, provisioned concurrency keeps a specified number of execution environments warm and ready to respond immediately. This provides a balance between the cost-effectiveness of serverless and the performance predictability of provisioned instances. Multi-model endpoints (Option A) can introduce " noisy neighbor " issues and latency spikes, while asynchronous inference (Option D) is intended for long-running workloads and cannot meet sub-500 ms requirements.
問題 #66
A company upgraded its Amazon Bedrock-powered foundation model (FM) that supports a multilingual customer service assistant. After the upgrade, the assistant exhibited inconsistent behavior across languages.
The assistant began generating different responses in some languages when presented with identical questions.
The company needs a solution to detect and address similar problems for future updates. The evaluation must be completed within 45 minutes for all supported languages. The evaluation must process at least 15,000 test conversations in parallel. The evaluation process must be fully automated and integrated into the CI/CD pipeline. The solution must block deployment if quality thresholds are not met.
Which solution will meet these requirements?
答案：C
解題說明：
Option D is the correct solution because it directly evaluates multilingual output consistency and quality in an automated, scalable, and deployment-gating workflow. Amazon Bedrock model evaluation jobs are designed to run large-scale, repeatable evaluations against defined datasets and to produce quantitative metrics that can be used as objective release criteria.
The core issue is semantic inconsistency across languages for equivalent inputs. The most reliable way to detect this is to create standardized test conversations where each language version expresses the same intent and constraints. Running those tests through the updated model and comparing results with similarity metrics (for example, semantic similarity between expected and actual answers, or between language variants) surfaces regressions that infrastructure testing cannot detect.
Bedrock evaluation jobs support running evaluations at scale and are well suited for processing large datasets quickly. By parallelizing evaluation runs across languages and conversations, the company can meet the 45- minute requirement while executing at least 15,000 conversations. Because the process is standardized, it also allows consistent baseline comparisons across releases.
Applying hallucination thresholds ensures that answers remain grounded and do not introduce fabricated details, which is particularly important when language-specific behavior shifts after a model upgrade.
Integrating evaluation jobs into the CI/CD pipeline enables fully automated execution on every model or configuration update. The pipeline can enforce a hard quality gate that blocks deployment if thresholds are not met, preventing regressions from reaching production.
Option A focuses on performance and infrastructure bottlenecks, not multilingual response quality. Option B is post-deployment and too slow to prevent regressions. Option C normalizes inputs but does not measure multilingual output equivalence or provide robust, quantitative gating.
Therefore, Option D best meets the automation, scale, timing, and deployment-blocking requirements.
問題 #67
An ecommerce company is developing a generative AI application that uses Amazon Bedrock with Anthropic Claude to recommend products to customers. Customers report that some recommended products are not available for sale on the website or are not relevant to the customer. Customers also report that the solution takes a long time to generate some recommendations.
The company investigates the issues and finds that most interactions between customers and the product recommendation solution are unique. The company confirms that the solution recommends products that are not in the company's product catalog. The company must resolve these issues.
Which solution will meet this requirement?
答案：A
解題說明：
Option C best addresses both core problems: hallucinated recommendations that do not exist in the catalog and slow response times, while keeping operational overhead low. The most direct way to prevent the model from recommending unavailable products is to ground generation on authoritative product catalog data at inference time. An Amazon Bedrock knowledge base is designed for this pattern by ingesting domain data, chunking content, creating embeddings, and retrieving the most relevant catalog entries when a user asks for recommendations. Implementing Retrieval Augmented Generation ensures the foundation model receives only approved, catalog-backed context and can cite or base its output on those retrieved items. This sharply reduces the likelihood of inventing products, because the response is conditioned on retrieved catalog records rather than relying on the model's parametric memory.
The requirement also notes that most interactions are unique. That makes response caching far less effective, because there are fewer repeated prompts to benefit from cached outputs. Instead, improving the retrieval and model invocation path is the better optimization. Using the PerformanceConfigLatency parameter set to optimized prioritizes lower latency behavior for model inference, helping meet faster recommendation generation without requiring the company to build and operate additional infrastructure.
The other options do not solve the root cause as reliably. Prompt engineering and streaming can improve perceived latency, but they do not guarantee catalog-only recommendations because the model can still hallucinate items. Guardrails can help detect or block certain undesired outputs, but without consistent catalog grounding they do not ensure every recommendation is derived from the company's product data. Building a custom OpenSearch validation and caching layer increases operational complexity, and caching is misaligned with predominantly unique interactions.
問題 #68
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