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NEW QUESTION # 99
A financial services company is developing a customer service AI assistant application that uses a foundation model (FM) in Amazon Bedrock. The application must provide transparent responses by documenting reasoning and by citing sources that are used for Retrieval Augmented Generation (RAG). The application must capture comprehensive audit trails for all responses to users. The application must be able to serve up to
10,000 concurrent users and must respond to each customer inquiry within 2 seconds.
Which solution will meet these requirements with the LEAST operational overhead?
Answer: C
Explanation:
Option A is the correct solution because it relies on native Amazon Bedrock capabilities to deliver transparency, auditability, scalability, and low latency with minimal operational overhead. Amazon Bedrock Knowledge Bases provide a fully managed Retrieval Augmented Generation (RAG) implementation that automatically handles document ingestion, embedding, retrieval, and source attribution, enabling the application to cite authoritative content without building custom pipelines.
Enabling tracing for Amazon Bedrock Agents provides end-to-end visibility into agent reasoning steps, tool usage, and model interactions. This satisfies the requirement for comprehensive audit trails and supports regulatory review in financial services environments. Structured prompts further ensure that responses explicitly present reasoning and supporting evidence in a controlled, auditable format.
Using Amazon API Gateway and AWS Lambda allows the application to scale automatically to thousands of concurrent users without capacity planning. These services are designed for bursty workloads and can easily support the stated requirement of up to 10,000 concurrent users. Amazon CloudFront reduces latency by caching and accelerating content delivery, helping the application meet the strict 2-second response-time requirement.
Option B introduces a custom RAG pipeline with OpenSearch, increasing operational complexity and maintenance effort. Option C lacks native RAG integration and does not provide transparent reasoning or citation management. Option D focuses on offline compliance reporting rather than real-time transparency and low-latency responses.
Therefore, Option A best meets all requirements while minimizing infrastructure and operational overhead.
NEW QUESTION # 100
A financial services company uses multiple foundation models (FMs) through Amazon Bedrock for its generative AI (GenAI) applications. To comply with a new regulation for GenAI use with sensitive financial data, the company needs a token management solution.
The token management solution must proactively alert when applications approach model-specific token limits. The solution must also process more than 5,000 requests each minute and maintain token usage metrics to allocate costs across business units.
Which solution will meet these requirements?
Answer: C
NEW QUESTION # 101
A medical device company wants to feed reports of medical procedures that used the company's devices into an AI assistant. To protect patient privacy, the AI assistant must expose patient personally identifiable information (PII) only to surgeons. The AI assistant must redact PII for engineers. The AI assistant must reference only medical reports that are less than 3 years old.
The company stores reports in an Amazon S3 bucket as soon as each report is published. The company has already set up an Amazon Bedrock Knowledge Bases. The AI assistant uses Amazon Cognito to authenticate users.
Which solution will meet these requirements?
Answer: C
Explanation:
Option C is the correct solution because it enforces privacy controls at inference time, not at ingestion time, which is required when different user roles require different visibility into the same underlying data.
Using an S3 Lifecycle configuration ensures that documents older than 3 years are automatically removed, guaranteeing that the knowledge base references only compliant, recent medical reports. Scheduling Lambda- based syncs keeps the knowledge base aligned with the bucket contents without introducing complex per- upload orchestration.
The most important requirement is role-based PII exposure. Amazon Bedrock guardrails support dynamic application at inference time, allowing the system to select a guardrail configuration based on the authenticated user's Amazon Cognito group. Surgeons can receive full responses, while engineers receive responses with PII masked-without duplicating data or maintaining multiple knowledge bases.
This approach preserves a single source of truth for medical reports while enforcing privacy through response- level controls. It also maintains full auditability of access and redaction behavior.
Option A permanently removes PII and violates surgeon access requirements. Option B redacts data inconsistently and couples privacy logic to ingestion. Option D doubles storage, increases cost, and introduces data drift risk.
Therefore, Option C best meets privacy, compliance, scalability, and operational efficiency requirements.
NEW QUESTION # 102
A financial services company uses an AI application to process financial documents by using Amazon Bedrock. During business hours, the application handles approximately 10,000 requests each hour, which requires consistent throughput.
The company uses the CreateProvisionedModelThroughput API to purchase provisioned throughput. Amazon CloudWatch metrics show that the provisioned capacity is unused while on-demand requests are being throttled. The company finds the following code in the application:
python
response = bedrock_runtime.invoke_model(modelId="anthropic.claude-v2", body=json.dumps(payload)) The company needs the application to use the provisioned throughput and to resolve the throttling issues.
Which solution will meet these requirements?
Answer: B
Explanation:
Option B is correct because the application is currently invoking the base foundation model identifier, which routes traffic to the on-demand capacity pool rather than the company's purchased provisioned throughput. In Amazon Bedrock, provisioned throughput is attached to a specific provisioned resource created through the provisioned throughput APIs. To consume that reserved capacity, inference requests must target the provisioned resource identifier that represents the purchased throughput, not the generic model identifier used for on-demand inference.
The code snippet uses modelId="anthropic.claude-v2". This value selects the on-demand endpoint for that model. As a result, requests are subject to on-demand quotas and throttling behavior, while the provisioned throughput remains idle. This directly explains the CloudWatch observation: provisioned capacity metrics show unused capacity because no traffic is being directed to the provisioned resource, and the on-demand path is throttling because it is exceeding the applicable on-demand limits during peak volume.
Replacing the modelId value with the provisioned throughput ARN returned by the CreateProvisionedModelThroughput workflow ensures the runtime invocation is routed to the reserved capacity. Once traffic is directed correctly, the purchased model units provide the consistent throughput required for predictable performance during business hours, which is exactly why provisioned throughput is used.
Option A could increase capacity, but it does not fix the core issue that the application is not using the provisioned resource at all. Option C can reduce the impact of throttling temporarily, but it adds latency and does not guarantee consistent throughput; it also still wastes the provisioned capacity. Option D changes the response delivery mechanism, but throttling is a capacity routing and quota issue, not a streaming API issue.
NEW QUESTION # 103
A financial services company wants to develop an Amazon Bedrock application that gives analysts the ability to query quarterly earnings reports and financial statements. The financial documents are typically 5-100 pages long and contain both tabular data and text. The application must provide contextually accurate responses that preserve the relationship between financial metrics and their explanatory text. To support accurate and scalable retrieval, the application must incorporate document segmentation and context management strategies.
Which solution will meet these requirements?
Answer: D
Explanation:
Option B best satisfies the requirements because it directly applies Retrieval Augmented Generation principles using managed Amazon Bedrock Knowledge Bases, which are designed to handle large, complex documents while preserving contextual relationships. Financial reports often interleave tables with explanatory narrative, and accurate analysis depends on keeping those elements logically connected. By segmenting documents based on their structural layout-for example, sections, subsections, tables, and surrounding commentary-the knowledge base can retrieve semantically relevant chunks that maintain this relationship during inference.
Amazon Bedrock Knowledge Bases support contextual chunking strategies that go beyond simple fixed-size segmentation. This is critical for financial documents, where a metric in a table may be explained in adjacent paragraphs or footnotes. Context-aware chunking ensures that retrieved content includes both the numeric data and its interpretation, enabling the foundation model to generate accurate, grounded responses. Including citations further improves analyst trust and auditability by allowing users to trace answers back to specific source sections, which is a common requirement in financial environments.
Scalability is another key requirement. Knowledge Bases manage embedding generation, indexing, and retrieval orchestration as a managed service, which allows the solution to scale across large document collections without requiring custom infrastructure or model hosting. This approach also supports efficient updates as new quarterly reports are added, ensuring the retrieval layer remains current.
Option A does not scale well because processing entire 5-100 page documents in a single prompt increases token usage, latency, and cost while risking context truncation. Option C relies on fixed-size chunking triggered at query time, which often breaks semantic relationships in structured financial content. Option D introduces unnecessary architectural complexity by splitting structured and unstructured data into separate applications, increasing operational overhead without providing better contextual retrieval than a unified RAG approach.
NEW QUESTION # 104
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