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問題 #110
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.)
答案：B,E
解題說明：
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.
問題 #111
A company has set up Amazon Q Developer Pro licenses for all developers at the company. The company maintains a list of approved resources that developers must use when developing applications. The approved resources include internal libraries, proprietary algorithmic techniques, and sample code with approved styling.
A new team of developers is using Amazon Q Developer to develop a new Java-based application. The company must ensure that the new developer team uses the company's approved resources. The company does not want to make project-level modifications.
Which solution will meet these requirements?
答案：C
解題說明：
Option D is the correct solution because Amazon Q Developer customizations are designed to incorporate organization-approved knowledge and coding guidance without requiring per-project changes. A customization can point Amazon Q Developer to curated internal sources such as approved libraries, coding standards, architectural patterns, and proprietary techniques. This allows the assistant's suggestions to align with company policies and preferred implementations consistently across teams and repositories.
The key requirement is that the company does not want to make project-level modifications. Options A, B, and C all require adding files or repositories into the project workspace, which directly violates this constraint.
They also rely on developer behavior to "use workspace context," which is harder to enforce and can lead to inconsistent adherence to standards.
With a customization, the organization centrally manages and updates approved resources. This reduces operational overhead because updates to libraries, patterns, or guidelines propagate automatically to developers using the customization, without requiring changes to each project. This is especially valuable for a new team, where consistent enforcement of approved practices is important to reduce compliance risk, security issues, and inconsistent code style.
Additionally, customizations support governance by allowing the company to standardize how Amazon Q Developer responds, ensuring that suggestions reflect approved internal content rather than generic public patterns.
Therefore, Option D best satisfies the requirement for centralized enforcement of approved resources with minimal ongoing management and no project-level modifications.
問題 #112
A company is using AWS Lambda and REST APIs to build a reasoning agent to automate support workflows.
The system must preserve memory across interactions, share relevant agent state, and support event-driven invocation and synchronous invocation. The system must also enforce access control and session-based permissions.
Which combination of steps provides the MOST scalable solution? (Select TWO.)
答案：A,B
解題說明：
The combination of Options A and B provides the most scalable and AWS-native architecture for building reasoning agents with persistent memory, session awareness, secure access control, and flexible invocation models.
Amazon Bedrock AgentCore is purpose-built to manage agent memory, session context, and identity-aware reasoning across interactions. It eliminates the need for developers to manually store and retrieve agent state, manage session lifecycles, or implement custom memory layers. AgentCore natively supports both synchronous requests and event-driven execution, making it ideal for support workflow automation.
Option B complements AgentCore by enabling seamless tool invocation. By registering AWS Lambda functions and REST APIs as agent actions through API Gateway and EventBridge, the agent can invoke tools reactively or synchronously without custom orchestration code. EventBridge enables event-driven execution, while API Gateway supports synchronous request-response patterns.
This combination provides built-in security, observability, and scaling, while avoiding the operational burden of managing queues, databases, or custom workflow engines.
Option C introduces unnecessary orchestration complexity. Option D increases infrastructure management and cost. Option E stores agent state in S3, which is not suitable for low-latency, session-based reasoning.
Therefore, A and B together deliver the most scalable, secure, and low-overhead solution for production- grade reasoning agents on AWS.
問題 #113
A GenAI developer is building a Retrieval Augmented Generation (RAG)-based customer support application that uses Amazon Bedrock foundation models (FMs). The application needs to process 50 GB of historical customer conversations that are stored in an Amazon S3 bucket as JSON files. The application must use the processed data as its retrieval corpus. The application's data processing workflow must extract relevant data from customer support documents, remove customer personally identifiable information (PII), and generate embeddings for vector storage. The processing workflow must be cost-effective and must finish within 4 hours.
Which solution will meet these requirements with the LEAST operational overhead?
答案：C
解題說明：
Option D is the best solution because it delivers a fully managed, scalable pipeline with minimal infrastructure management while meeting the 50 GB and 4-hour constraint. AWS Step Functions provides a serverless orchestration layer that can coordinate parallel processing steps, retries, and error handling without managing clusters or tuning long-running compute.
Using Amazon Comprehend for PII detection fulfills the requirement to remove customer PII in a managed and consistent way. Step Functions can coordinate Comprehend calls at scale and route sanitized outputs into the embedding step. Generating embeddings with Amazon Bedrock keeps the entire workflow within AWS managed services, eliminates the need to maintain custom embedding models, and supports consistent vector representations for downstream retrieval.
Direct integration with Amazon OpenSearch Serverless provides a low-operations vector store that can handle large-scale indexing and similarity search without cluster sizing, node maintenance, or shard management.
This aligns strongly with the requirement for least operational overhead and supports growth beyond the initial 50 GB corpus. Step Functions can batch and parallelize ingestion into OpenSearch Serverless to meet the 4-hour completion goal in a cost-effective manner by controlling concurrency, chunk sizes, and failure handling.
Option A can be difficult and costly at this scale because Lambda concurrency and per-invocation overhead can become complex to tune for 50 GB within 4 hours. Option B introduces SageMaker Processing and embedding model management, increasing operational complexity. Option C requires EMR cluster management and tuning, which is the opposite of minimal overhead.
Therefore, Option D is the most operationally efficient, scalable, and managed approach to build the required PII-sanitized embedding pipeline for a RAG corpus.
問題 #114
An insurance company is using Amazon Bedrock to build a claims processing application. The application must perform the following steps in sequence: analyze documents, extract data, and generate recommendations. Claims over $10,000 require an additional fraud analysis step before the application provides a recommendation.
Which solution will meet these requirements with the LEAST operational complexity?
答案：A
解題說明：
Amazon Bedrock Flows-referred to as "Amazon Bedrock Prompt Flows" in the question-is the lowest- complexity solution because the workflow consists primarily of generative-AI processing stages and one straightforward conditional branch. Bedrock Flows provides a managed visual workflow environment in which nodes represent steps that invoke Amazon Bedrock or related resources. This eliminates the need to build a separate orchestration service merely to connect multiple model interactions.
A prompt node defines a prompt, receives values as input variables, invokes the configured model, and produces the model completion as its output. Therefore, separate prompt nodes can represent document analysis, structured data extraction, fraud analysis, and recommendation generation.
A condition node provides deterministic branching. AWS documents relational operators including > , > = , <
, < = , == , and != , and the node can direct its input to different downstream nodes based on the configured condition. The application can therefore test whether the claim amount is greater than $10,000. Claims above that threshold are routed through the fraud-analysis node before reaching the recommendation stage; other claims can go directly to recommendation generation.
A Step Functions implementation in A would work technically and is appropriate when orchestration spans a broad set of distributed AWS services. However, for a Bedrock-centric GenAI workflow, it adds another orchestration layer and therefore is not the least-complex solution. B unnecessarily introduces autonomous agent reasoning for deterministic sequential processing. D requires custom Lambda invocation code, error handling, routing logic, deployment, and maintenance.
Because the required workflow maps directly to Bedrock ' s managed prompt and condition nodes, C minimizes infrastructure and custom orchestration while preserving deterministic control over the $10,000 fraud-analysis requirement.
問題 #115
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