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NEW QUESTION # 113
A financial services company needs to build a document analysis system that uses Amazon Bedrock to process quarterly reports. The system must analyze financial data, perform sentiment analysis, and validate compliance across batches of reports. Each batch contains 5 reports. Each report requires multiple foundation model (FM) calls. The solution must finish the analysis within 10 seconds for each batch. Current sequential processing takes 45 seconds for each batch.
Which solution will meet these requirements?
Answer: D
Explanation:
Option B is the correct solution because it parallelizes independent foundation model inference tasks while maintaining orchestration, observability, and time-bound execution. AWS Generative AI best practices emphasize reducing end-to-end latency by parallelizing independent inference calls rather than scaling individual calls vertically.
In this scenario, each report requires multiple independent analyses such as financial extraction, sentiment analysis, and compliance validation. These tasks do not depend on each other's output, making them ideal candidates for parallel execution. AWS Step Functions provides a Parallel state that can invoke multiple AWS Lambda functions simultaneously, drastically reducing total processing time compared to sequential execution.
By invoking Amazon Bedrock from separate Lambda functions in parallel, the system can reduce batch execution time from 45 seconds to well under the 10-second requirement, assuming each inference call remains within acceptable latency bounds. Step Functions also provide built-in error handling, retries, and state tracking, which improves reliability without increasing complexity.
CloudWatch metrics allow teams to monitor both workflow execution time and individual model inference latency, enabling performance tuning and operational visibility. Configuring client-side timeouts ensures that slow or failed model invocations do not block the entire batch.
Option A still processes tasks sequentially and therefore cannot meet the strict latency requirement. Option C introduces queuing delays and sequential processing within each report, which increases total execution time.
Option D relies on container-based sequential processing and adds unnecessary operational overhead for a workload that is event-driven and latency-sensitive.
Therefore, Option B best meets the performance, scalability, and operational efficiency requirements for high- speed batch document analysis using Amazon Bedrock.
NEW QUESTION # 114
A company is building a meeting analysis solution for its executive team. The solution uses AWS generative AI services. The solution must extract speaker-attributed content from recorded meetings, analyze visual elements from presentation slides, and create searchable summaries that link speaker comments to relevant visual context.
The solution must process 200 hours of meeting recordings each week. The solution must maintain data privacy by processing all meeting data within the AWS Cloud. The solution must store the source data for future retrieval and must be able to perform full-text searches.
Which solution will meet these requirements with the LEAST operational overhead?
Answer: A
Explanation:
Option B provides the strongest match among the listed alternatives because it combines managed speaker identification, multimodal generative analysis, and a native search-oriented datastore without unnecessary custom orchestration.
Amazon Transcribe speaker diarization distinguishes speakers and assigns identifiers such as spk_0 , spk_1 , and so forth. Its output includes speaker labels and timestamps, allowing individual statements to be associated with particular speakers and positions in the meeting timeline.
Anthropic Claude models on Amazon Bedrock support multimodal prompts that combine text and images.
The application can therefore supply speaker-attributed transcript segments together with extracted presentation/video frames and instruct the model to generate summaries that associate spoken comments with relevant visual information. AWS ' s Claude Messages API documentation explicitly supports mixed image- and-text input.
Amazon OpenSearch Service is appropriate for storing the enriched meeting records and supporting document indexing and full-text searching. It provides substantially more natural full-text retrieval than attempting to create a custom indexing layer over DynamoDB.
A uses Transcribe and Rekognition effectively, but it states that a Lambda function itself "generates" the summaries without specifying a generative model and requires custom correlation logic. C chains BDA, Transcribe, Rekognition, DynamoDB, and Bedrock, increasing service count and operational complexity; DynamoDB also does not natively satisfy the full-text-search requirement. D similarly depends on DynamoDB plus a custom indexing mechanism.
Current AWS capabilities make Bedrock Data Automation even more capable than the wording of C suggests: BDA can produce video summaries, scene-level summaries, detected video text, full audio transcripts, and speaker identification. However, among the supplied choices , B remains the cleanest architecture satisfying multimodal GenAI analysis, diarization, scalable managed processing, and native searchable storage.
NEW QUESTION # 115
A company is using Amazon Bedrock to develop an AI-powered application that uses a foundation model that supports cross-Region inference and provisioned throughput. The application must serve users in Europe and North America with consistently low latency. The application must comply with data residency regulations that require European user data to remain within Europe-based AWS Regions.
During testing, the application experiences service degradation when Regional traffic spikes reach service quotas. The company needs a solution that maintains application resilience and minimizes operational complexity.
Which solution will meet these requirements?
Answer: D
Explanation:
Option B best meets the latency, resilience, and data residency requirements while keeping operational complexity low by using built-in Amazon Bedrock cross-Region inference behavior through inference profiles. Cross-Region inference profiles are designed to provide higher availability and better traffic absorption when a single Region experiences throttling, transient capacity constraints, or quota-related degradation. By selecting the appropriate geography-scoped inference profile (for example, a Europe-scoped profile for European users and a North America-scoped profile for North American users), the application can keep inference traffic within the required geographic boundary. This directly supports EU data residency needs because European requests can be served only by Europe-based Regions while still benefiting from multi-Region resilience inside Europe.
The question also highlights degradation when Regional traffic spikes hit quotas. Cross-Region inference profiles help mitigate these conditions by allowing Bedrock to serve requests from another Region within the same geography, improving continuity during spikes without requiring the company to implement custom retry-and-failover logic across Regions. This reduces development and operational burden compared to building and maintaining a bespoke routing and fallback system.
Using separate Amazon API Gateway HTTP APIs to direct European and North American users to the correct endpoints simplifies request routing and provides a clean boundary for compliance controls, logging, and monitoring. It also allows each geography to scale independently and maintain consistently low latency by keeping users close to the entry point and the Bedrock geography they must use.
Option A requires custom routing and manual operational monitoring and does not inherently solve quota- driven degradation. Option C adds significant complexity by embedding throttling retries and cross-Region selection logic in Lambda while still needing careful controls to prevent cross-border routing mistakes. Option D introduces the highest operational complexity and can inadvertently violate residency if failover crosses geographies unless additional safeguards are implemented.
NEW QUESTION # 116
A company is using Amazon Bedrock to develop a customer support AI assistant. The AI assistant must respond to customer questions about their accounts. The AI assistant must not expose personal information in responses. The company must comply with data residency policies by ensuring that all processing occurs within the same AWS Region where each customer is located.
The company wants to evaluate how effective the AI assistant is at preventing the exposure of personal information before the company makes the AI assistant available to customers.
Which solution will meet these requirements?
Answer: B
Explanation:
Option B best meets all stated requirements by correctly combining PII protection , evaluation before launch , and data residency compliance using Amazon Bedrock Guardrails. Amazon Bedrock guardrails provide native sensitive information filtering that operates inline during model invocation, making them well suited for preventing personal data exposure in customer-facing AI assistants.
The requirement to evaluate how effective the AI assistant is at preventing exposure before release is best addressed by using mask mode during development and testing . Mask mode allows responses to be generated while automatically redacting detected personal information, making it easy for developers and reviewers to see where and how PII would have appeared. This provides concrete validation that the guardrail rules are correctly configured without fully blocking responses, which is ideal for quality assurance and pre- production evaluation.
For production , switching the guardrail to block mode ensures that responses containing personal information are fully prevented from being returned to users. This offers the strongest protection and aligns with compliance expectations for customer account data. Block mode is appropriate once confidence in the guardrail configuration has been established during testing.
The data residency requirement is addressed by deploying a copy of the guardrail in each AWS Region where the application operates. Amazon Bedrock guardrails are Region-specific resources, and using Region- local guardrails ensures that inference, filtering, and enforcement all occur within the same Region as the customer data. This avoids cross-Region processing and helps the company comply with regulatory and contractual data residency policies.
Option A and D incorrectly rely on cross-Region guardrails, which can violate data residency constraints.
Option C focuses on topic filtering rather than sensitive information filtering and keeps detect mode enabled in production, which does not actively prevent PII exposure. Therefore, B is the only option that fully satisfies safety, compliance, and evaluation requirements.
NEW QUESTION # 117
A healthcare company runs a multi-step workflow that uses Amazon Bedrock foundation models (FMs) to assist clinicians with making patient clinical decisions. Recent data shows that 25% of decision processes fail to finish. However, the company cannot identify which specific workflow step is causing the failures.
The company needs to implement a monitoring solution to identify the exact failure points in the workflow.
The solution must provide detailed analytics for the completion rates of each workflow step.
Which solution will meet these requirements?
Answer: B
Explanation:
Option C is correct because Amazon Bedrock agent tracing exposes the internal execution path needed to identify precisely where a multi-step workflow fails. When tracing is enabled for InvokeAgent, the response can include structured trace events. AWS documents OrchestrationTrace for orchestration activity and FailureTrace for the reason a particular step failed. Those records can be aggregated to calculate per-step attempts, successes, failures, and completion rates. Option A monitors broad client/server error metrics; even with custom metrics, it does not inherently provide the same structured execution context. Option B uses CloudTrail, which is primarily an API audit trail rather than a step-by-step agent reasoning trace. Option D requires custom EventBridge instrumentation and storage. Native agent trace data therefore provides the most direct evidence for exact failure-point analysis and detailed workflow completion analytics. AWS Documentation
NEW QUESTION # 118
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