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NEW QUESTION # 45
A company uses Amazon Bedrock to generate technical content for customers. The company has recently experienced a surge in hallucinated outputs when the company's model generates summaries of long technical documents. The model outputs include inaccurate or fabricated details. The company's current solution uses a large foundation model (FM) with a basic one-shot prompt that includes the full document in a single input.
The company needs a solution that will reduce hallucinations and meet factual accuracy goals. The solution must process more than 1,000 documents each hour and deliver summaries within 3 seconds for each document.
Which combination of solutions will meet these requirements? (Select TWO.)
Answer: B,D
Explanation:
The correct answers are B and C because they directly address hallucination reduction while maintaining high throughput and low latency.
Option B reduces hallucinations at their source by grounding model outputs in verified content through Retrieval Augmented Generation (RAG). Using an Amazon Bedrock knowledge base with semantic chunking ensures that long technical documents are broken into meaningfully coherent sections. This allows the model to retrieve only the most relevant chunks, rather than processing an entire document in one pass, which significantly improves factual accuracy and reduces cognitive overload on the model. This approach scales efficiently and supports processing more than 1,000 documents per hour.
Option C adds a defense-in-depth safety layer by using Amazon Bedrock guardrails to detect and block hallucination-like output patterns. Guardrails operate at inference time with minimal performance overhead, making them suitable for low-latency requirements. While guardrails do not eliminate hallucinations entirely, they effectively prevent unsafe or clearly fabricated outputs from reaching users.
Option A increases latency and cost due to explicit reasoning steps and does not scale well for high- throughput workloads. Option D increases randomness and worsens hallucinations. Option E repeats the existing flawed approach.
Therefore, Options B and C together provide scalable grounding and runtime protection that meet accuracy, performance, and throughput requirements.
NEW QUESTION # 46
A company is using Amazon Bedrock to develop an AI-powered application that uses a foundation model (FM) 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: C
Explanation:
Option B is the most appropriate solution because it directly uses Amazon Bedrock cross-Region inference profiles, which are designed to provide resilience and load distribution while respecting data residency boundaries. Cross-Region inference profiles allow applications to distribute inference requests across multiple Regions within a defined geographic boundary, such as Europe or North America, without requiring custom failover logic.
By specifying geographical codes in the inference profile ID, the application ensures that European user data is processed only within Europe-based Regions, satisfying regulatory requirements. At the same time, Bedrock automatically routes requests to healthy Regions within that geography when traffic spikes or service quotas are reached, improving availability and maintaining low latency.
Using separate Amazon API Gateway HTTP APIs for Europe and North America provides a clean, simple routing layer that directs users to the appropriate regional inference profile. This avoids complex custom routing or retry logic in application code and minimizes operational overhead.
Option A relies on custom routing and manual monitoring, which increases complexity and does not provide automatic resilience. Option C introduces custom retry and fallback logic that risks violating data residency requirements if misconfigured. Option D requires significant application-level failover logic and adds operational burden with Global Accelerator configuration.
Therefore, Option B best meets the requirements for low latency, data residency compliance, resilience during traffic spikes, and minimal operational complexity.
NEW QUESTION # 47
A company is building an AI-powered customer support application that uses Amazon Bedrock FMs. The application must provide users with recommendations based on an internal Amazon Bedrock knowledge base.
Users will query documents that the company stores in Amazon S3 and structured data that is in Amazon RDS. The application must provide citations for the sources it uses to generate recommendations. The application must also provide secure access to sensitive data.
Which solution will meet these requirements?
Answer: D
NEW QUESTION # 48
A company is creating a workflow to review customer-facing communications before the company sends the communications. The company uses a pre-defined message template to generate the communications and stores the communications in an Amazon S3 bucket. The workflow needs to capture a specific portion from the template and send it to an Amazon Bedrock model. The workflow must store model responses back to the original S3 bucket.
Which solution will meet these requirements?
Answer: C
Explanation:
Option A is the correct answer because Amazon Bedrock Flows is purpose-built to orchestrate generative AI workflows that combine data access, deterministic transformations, and model invocation with minimal operational overhead. The requirements explicitly state that the workflow must retrieve content from Amazon S3, extract a specific portion of a predefined template, send that portion to an Amazon Bedrock model, and store the model's response back into the same S3 bucket. Amazon Bedrock Flows natively supports all of these steps.
By configuring S3 action nodes at the beginning and end of the flow, the workflow can retrieve the original communications and persist the reviewed output without custom code. The expression step allows deterministic parsing of a specific portion of the template, which is essential when only part of the message should be reviewed. This avoids relying on generative logic for parsing, which would be less predictable and harder to audit. The agent step is then used specifically for the review task, where the foundation model evaluates or modifies the extracted content.
Option B uses AWS Step Functions, which can achieve similar outcomes but requires more explicit orchestration logic and does not provide GenAI-native constructs such as expressions and agent steps in a single managed experience. Options C and D rely on Amazon Bedrock agents and AWS Lambda functions to handle parsing and data movement, which increases complexity, operational overhead, and maintenance burden.
Because Amazon Bedrock Flows directly integrates S3 actions, parsing expressions, and model review steps in a single managed workflow, Option A best meets the requirements with the least development and operational effort.
NEW QUESTION # 49
A company is using Amazon Bedrock to design an application to help researchers apply for grants. The application is based on an Amazon Nova Pro foundation model (FM). The application contains four required inputs and must provide responses in a consistent text format. The company wants to receive a notification in Amazon Bedrock if a response contains bullying language. However, the company does not want to block all flagged responses.
The company creates an Amazon Bedrock flow that takes an input prompt and sends it to the Amazon Nova Pro FM. The Amazon Nova Pro FM provides a response.
Which additional steps must the company take to meet these requirements? (Select TWO.)
Answer: B,E
Explanation:
The correct answers are A and D because they collectively satisfy the requirements for structured inputs, consistent output formatting, and non-blocking detection of bullying language.
Option A is required because Amazon Bedrock Prompt Management enables prompt templates with explicit input variables and defined output formats. By defining the four required inputs as variables, the company ensures that every invocation of the Amazon Nova Pro FM receives the correct structured inputs. Specifying the output format ensures consistent responses, which is essential for a grants application workflow. Adding the managed prompt to the prompts node of the flow allows Bedrock Flows to invoke the model using this standardized configuration.
Option D addresses the requirement to receive notifications when bullying language is detected without blocking responses. Amazon Bedrock guardrails support content filters with configurable actions. By applying the insults content filter and setting the response action to detect, the system flags responses containing bullying or insulting language while still allowing the response to be returned. This enables monitoring, alerting, and auditing without interrupting application functionality.
Option B is incorrect because setting the filter response to block contradicts the requirement not to block all flagged responses. Option C introduces a prompt router, which is unnecessary because the application uses a single Amazon Nova Pro FM. Option E incorrectly attempts to enforce input variables and output formatting through an inference profile, which does not provide prompt-level variable enforcement or formatting guarantees.
Therefore, A and D together provide structured prompt management and non-blocking safety detection with minimal operational complexity.
NEW QUESTION # 50
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