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NEW QUESTION # 45
A financial services company is using Amazon Bedrock to deploy a GenAI application across multiple business units. The company must ensure that all prompts that are used with the application ' s FMs follow regulatory compliance standards and maintain consistent formatting.
The company must implement a solution that provides version control for prompt templates, requires approval workflows for new prompts, and maintains detailed audit trails of all prompt usage and modifications.
Which combination of solutions will meet these requirements? (Select TWO.)
Answer: B,C
Explanation:
Options A and C provide the most AWS-native governance approach. Amazon Bedrock Prompt Management supports reusable parameterized prompts and explicit prompt versions, allowing teams to preserve approved configurations and deploy stable versions instead of uncontrolled working drafts. An organizational approval gate can therefore be applied before a new prompt version becomes production-approved. AWS CloudTrail records Amazon Bedrock API activity, including the identity making the request, the operation performed, time, source information, and other event details, providing the required audit trail for Bedrock resource modifications and use. CloudWatch can complement this with operational usage monitoring and reporting.
Option B recreates prompt governance through S3, Lambda, and SNS. Option D uses general-purpose configuration storage rather than Bedrock-native prompt management. Option E adds substantial custom state and filtering logic without providing the same managed prompt-version lifecycle. AWS Documentation
NEW QUESTION # 46
A financial services company is developing an AI-powered search assistant application to help investment advisors quickly retrieve investment data. The application runs as an AWS Lambda function. The company is using Amazon Bedrock to develop the application by using an Amazon Bedrock knowledge base that uses Amazon OpenSearch Serverless as its data source. The application agent must manage collections at scale by automatically assigning access permissions to collections and indexes that match a specific pattern. The company uses Amazon Bedrock tools to test the knowledge base. The knowledge base sync process finishes successfully. However, the test reveals a 400 Bad Authorization error from the BedrockAgentRuntime API and a 403 Forbidden error when the test attempts to access OpenSearch Serverless. The company must resolve the permissions issues. Which combination of solutions will meet this requirement? (Select TWO.)
Answer: A,B
Explanation:
The errors described indicate missing permissions at both the application orchestration and data access levels.
The 400 Bad Authorization from BedrockAgentRuntime indicates the Lambda execution role lacks the identity permission to invoke the agent; adding bedrock:InvokeAgent and aoss:APIAccessAll (which allows the principal to interact with OpenSearch Serverless APIs) is necessary. The 403 Forbidden error from OpenSearch Serverless specifically relates to data-plane permissions. Unlike traditional OpenSearch, Serverless uses data access policies . To " manage collections at scale " automatically, a policy must be created that uses pattern-based resource rules (e.g., matching a prefix), ensuring that as new collections or indexes are created, the required principals (the Lambda role and the Bedrock service role) are granted the necessary access without manual policy updates for every new resource.
NEW QUESTION # 47
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: D,E
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 # 48
A company is creating a generative AI (GenAI) application that uses Amazon Bedrock foundation models (FMs). The application must use Microsoft Entra ID to authenticate. All FM API calls must stay on private network paths. Access to the application must be limited by department to specific model families. The company also needs a comprehensive audit trail of model interactions.
Which solution will meet these requirements?
Answer: D
Explanation:
Option A is the correct solution because it satisfies authentication, private connectivity, fine-grained authorization, and auditing using AWS-recommended patterns.
SAML federation between Microsoft Entra ID and IAM is a mature, well-supported integration that enables centralized enterprise authentication. Department-specific IAM roles allow precise control over which Bedrock ModelId values each department can invoke, enforcing access by model family.
Using AWS PrivateLink interface VPC endpoints for Amazon Bedrock runtime services ensures that all inference traffic stays on private AWS network paths, with no public internet exposure. NAT gateways and public endpoints, as used in other options, violate this requirement.
AWS CloudTrail provides authoritative audit logs of all Bedrock API calls, which is required for compliance.
Amazon Bedrock model invocation logging complements CloudTrail by capturing detailed prompt and response metadata for deeper auditing and investigation.
Option B uses public endpoints via NAT. Option C incorrectly claims public endpoints can be private. Option D relies on IdP-side logs, which do not capture Bedrock API activity.
Therefore, Option A is the only solution that fully meets security, compliance, and observability requirements.
NEW QUESTION # 49
A multinational bank wants to implement a RAG solution on AWS to run queries on internal policy and compliance documents. To comply with data residency regulations, the company must ensure that critical customer data remains within a specific AWS Region. The bank wants to use foundation models (FMs) on AWS to reduce infrastructure costs and minimize model maintenance.
Which solution will meet these requirements?
Answer: D
Explanation:
Option A is the appropriate AWS-native approach because it keeps the authoritative documents in an Amazon S3 bucket located in the regulated Region while using managed Amazon Bedrock foundation-model infrastructure instead of maintaining containerized models. Amazon Bedrock supports private connectivity through AWS PrivateLink interface VPC endpoints. Applications inside the VPC can therefore reach Bedrock privately without requiring an internet gateway, NAT gateway, public IP address, or public internet path.
Amazon Bedrock Knowledge Bases can use Amazon S3 as a RAG data source. AWS requires the S3 bucket used by a knowledge base to be in the same Region as the Amazon Bedrock knowledge base. During ingestion, source content is converted into vector embeddings and stored in the configured vector store so similarity-based retrieval can locate relevant passages for augmentation. This creates an AWS-managed RAG architecture while preserving explicit regional placement of the underlying source documents.
Option B introduces AWS Outposts unnecessarily. More importantly, AWS documentation states that S3 on Outposts object data physically remains on the Outpost and "is not in an AWS Region." That conflicts with a requirement written specifically as keeping data within a particular AWS Region and also introduces additional infrastructure and operational cost.
C fails because "a Region close to" the regulated Region does not meet a requirement for the specified Region. Encryption does not override residency requirements. D requires the company to deploy and operate foundation-model containers, directly contradicting the objective of minimizing model infrastructure and maintenance.
Therefore, regional S3 storage combined with privately accessed managed Amazon Bedrock services provides the closest match to the stated residency, RAG, cost, and operational requirements.
NEW QUESTION # 50
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