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質問 # 79
A healthcare company is using Amazon Bedrock to build a system to help practitioners make clinical decisions. The system must provide treatment recommendations to physicians based only on approved medical documentation and must cite specific sources. The system must not hallucinate or produce factually incorrect information.
Which solution will meet these requirements with the LEAST operational overhead?
正解：B
解説：
Option B is the correct solution because Amazon Bedrock Knowledge Bases with the RetrieveAndGenerate API provide a fully managed Retrieval Augmented Generation (RAG) capability that directly addresses grounding, citation, and hallucination prevention with the least operational overhead.
Amazon Bedrock Knowledge Bases automatically manage document ingestion, chunking, embedding, retrieval, and ranking from approved data sources. When used with the RetrieveAndGenerate API, the model is constrained to generate responses only from retrieved, approved clinical documentation, significantly reducing the risk of hallucinations or unsupported claims. The API also returns explicit source citations, which satisfies regulatory and clinical transparency requirements without requiring custom comparison or validation logic.
This approach aligns with AWS best practices for healthcare GenAI workloads, where correctness and traceability are critical. Because retrieval and generation are tightly integrated, the system avoids multi-step orchestration, custom verification pipelines, or additional compute layers that would increase latency and maintenance burden.
Option A introduces Amazon Kendra and custom post-processing logic, increasing operational complexity.
Option C focuses on entity extraction rather than controlled knowledge grounding and does not guarantee citation or hallucination prevention. Option D requires manual orchestration between retrieval and generation and custom verification logic, which increases development and maintenance effort.
Therefore, Option B delivers accurate, grounded, and cited clinical recommendations with minimal infrastructure and operational overhead.
質問 # 80
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?
正解：D
解説：
Option B is correct because the workload has predictable short peak periods, strict low-latency requirements, and lower demand between spikes. Amazon SageMaker Serverless Inference is designed for intermittent or unpredictable traffic because SageMaker manages the infrastructure and the customer pays based on usage rather than continuously running idle instances. AWS documentation describes serverless inference as suitable when traffic is intermittent or unpredictable and when users do not want to manage instances or scaling policies.
Provisioned concurrency is the key part of this answer. For latency-sensitive serverless inference workloads, provisioned concurrency keeps the required number of serverless instances initialized and ready to respond.
AWS documentation states that SageMaker Serverless Inference integrates with Application Auto Scaling so provisioned concurrency can be scaled up or down based on a target metric or schedule. This fits a daily 2- hour peak window because the company can configure enough provisioned concurrency for 500 concurrent requests during peak periods and scale down when demand decreases.
Deploying each model to a separate serverless endpoint is also important. The categorization, extraction, and response-generation models likely have different memory, latency, and concurrency needs. Separate endpoints allow independent memory sizing, maximum concurrency configuration, monitoring, and scaling for each task. AWS API documentation also shows that serverless endpoint configuration includes maximum concurrency and provisioned concurrency settings, with provisioned concurrency required to be less than or equal to maximum concurrency.
Option A is less suitable because multi-model endpoints are usually best when many models can share infrastructure, but model loading and shared capacity can add latency risk. Option C assumes Amazon Bedrock provisioned throughput for models, but the company is developing specialized NLP models and needs task-specific deployment controls. Request batching can also add latency. Option D uses asynchronous inference, which is inappropriate for a less-than-500-ms synchronous response requirement. Therefore, option B best satisfies latency, concurrency, accuracy isolation, and resource optimization.
質問 # 81
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?
正解：D
解説：
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.
Alright, after comparing List B (txt file) against List A (Word file) , I have identified the unique questions.
These questions cover scenarios or architectural configurations that were not present in the existing list.
Here are the unique questions from List B, formatted as requested:
質問 # 82
A company is building a real-time voice assistant system to assist customer service representatives during customer calls. The system must convert audio calls to text with end-to-end latency of less than 500 ms. The system must use generative AI (GenAI) to produce response suggestions. Human supervisors must be able to rate the system ' s suggestions during a live customer call. The company must store all customer interactions to comply with auditing policies. Which solution will meet these requirements?
正解：B
解説：
To achieve the ultra-low latency requirement of less than 500 ms, the system must utilize streaming capabilities at every stage. Using Amazon Transcribe streaming with small (100-ms) audio chunks ensures that transcription begins immediately as the customer speaks. On the model side, Amazon Bedrock's InvokeModelWithResponseStream allows the application to receive tokens as they are generated, rather than waiting for the entire completion, which is critical for real-time interactions. Amazon DynamoDB is the ideal choice for storing supervisor ratings during a live call because it provides the single-digit millisecond latency required for high-frequency writes without impacting the application ' s performance. Options involving batch processing or SQS queuing are unsuitable for sub-500 ms interactive requirements.
質問 # 83
A financial services company provides an Amazon Bedrock-powered AI assistant that provides investment guidance to customers. Recent audits found racially and gender-biased responses. Auditors flagged several cases as potential regulatory compliance risks. The company must implement a 30-day remediation plan to eliminate biased outputs. The plan must preserve model accuracy and keep latency within acceptable customer-experience thresholds.
A GenAI developer must design a long-term solution that balances regulatory risks, operational scalability, and model-agnostic enforcement. The solution must apply enforceable at-inference-time controls so bias mitigation measures cannot be bypassed. The solution must provide logging for each inference for audit and regulatory reviews. The solution must support ongoing automated bias monitoring. The solution must not require any model re-training within 30 days. The solution must add minimal inference latency.
Which solution will meet these requirements?
正解：D
解説：
Option C most completely satisfies the mandatory controls. A model-independent fairness classifier can evaluate every generated response at inference time, enforce a blocking threshold, preserve classifier scores and associated metadata for auditability, and route questionable cases for human compliance review without retraining the underlying FM. Option B provides automated blocking but omits the stronger audit metadata and regulatory-review workflow explicitly required by the scenario. Option A is insufficient because SageMaker Clarify can monitor bias and publish bias metrics, but scheduled offline or periodic analysis does not provide a non-bypassable control on every Bedrock response. Option D is inappropriate because AWS WAF matching operates primarily on HTTP request components and pattern-based conditions; detecting prohibited words is not equivalent to evaluating demographic fairness or semantic bias in generated investment guidance.
質問 # 84
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